Overview

Brought to you by YData

Dataset statistics

Number of variables53
Number of observations40000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.2 MiB
Average record size in memory424.0 B

Variable types

Text1
DateTime6
Numeric23
Categorical22
Boolean1

Alerts

Check_Point has constant value "0.0" Constant
Accident_Type is highly overall correlated with Collision_Type and 1 other fieldsHigh correlation
Annual_Mileage is highly overall correlated with Low_Mileage_DiscountHigh correlation
Auto_Year is highly overall correlated with Vehicle_CostHigh correlation
Collision_Type is highly overall correlated with Accident_TypeHigh correlation
Injury_Claim is highly overall correlated with Total_ClaimHigh correlation
Low_Mileage_Discount is highly overall correlated with Annual_MileageHigh correlation
Num_of_Vehicles_Involved is highly overall correlated with Accident_TypeHigh correlation
Policy_BI_High is highly overall correlated with Policy_BI_LowHigh correlation
Policy_BI_Low is highly overall correlated with Policy_BI_HighHigh correlation
Property_Claim is highly overall correlated with Total_ClaimHigh correlation
Total_Claim is highly overall correlated with Injury_Claim and 2 other fieldsHigh correlation
Vehicle_Claim is highly overall correlated with Total_ClaimHigh correlation
Vehicle_Cost is highly overall correlated with Auto_YearHigh correlation
Garage_Location is highly imbalanced (94.7%) Imbalance
Commute_Discount is highly imbalanced (86.1%) Imbalance
Vehicle_Registration is uniformly distributed Uniform
Claim_ID has unique values Unique
Vehicle_Registration has unique values Unique

Reproduction

Analysis started2025-07-25 09:49:42.976529
Analysis finished2025-07-25 09:50:29.260013
Duration46.28 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Claim_ID
Text

Unique 

Distinct40000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
2025-07-25T15:20:29.435075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters400000
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40000 ?
Unique (%)100.0%

Sample

1st rowAA00000001
2nd rowAA00000002
3rd rowAA00000003
4th rowAA00000004
5th rowAA00000005
ValueCountFrequency (%)
aa00039985 1
 
< 0.1%
aa00039986 1
 
< 0.1%
aa00039987 1
 
< 0.1%
aa00039988 1
 
< 0.1%
aa00039989 1
 
< 0.1%
aa00039990 1
 
< 0.1%
aa00039991 1
 
< 0.1%
aa00039992 1
 
< 0.1%
aa00039993 1
 
< 0.1%
aa00039994 1
 
< 0.1%
Other values (39990) 39990
> 99.9%
2025-07-25T15:20:29.667961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 145999
36.5%
A 80000
20.0%
3 26000
 
6.5%
2 26000
 
6.5%
1 26000
 
6.5%
4 16001
 
4.0%
8 16000
 
4.0%
9 16000
 
4.0%
7 16000
 
4.0%
6 16000
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 145999
36.5%
A 80000
20.0%
3 26000
 
6.5%
2 26000
 
6.5%
1 26000
 
6.5%
4 16001
 
4.0%
8 16000
 
4.0%
9 16000
 
4.0%
7 16000
 
4.0%
6 16000
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 145999
36.5%
A 80000
20.0%
3 26000
 
6.5%
2 26000
 
6.5%
1 26000
 
6.5%
4 16001
 
4.0%
8 16000
 
4.0%
9 16000
 
4.0%
7 16000
 
4.0%
6 16000
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 400000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 145999
36.5%
A 80000
20.0%
3 26000
 
6.5%
2 26000
 
6.5%
1 26000
 
6.5%
4 16001
 
4.0%
8 16000
 
4.0%
9 16000
 
4.0%
7 16000
 
4.0%
6 16000
 
4.0%
Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
Minimum2022-01-01 00:00:00
Maximum2023-01-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-25T15:20:29.725472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:29.789479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)

Customer_Life_Value1
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.1884716 × 10-16
Minimum-1.601757
Maximum1.6021442
Zeros0
Zeros (%)0.0%
Negative18423
Negative (%)46.1%
Memory size312.6 KiB
2025-07-25T15:20:29.855990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.601757
5-th percentile-1.601757
Q1-0.80078174
median0.00019356903
Q30.80116888
95-th percentile1.6021442
Maximum1.6021442
Range3.2039012
Interquartile range (IQR)1.6019506

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)-4.5694561 × 1015
Kurtosis-1.2173609
Mean-2.1884716 × 10-16
Median Absolute Deviation (MAD)0.80097531
Skewness0.00046700601
Sum-8.7538865 × 10-12
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:29.914508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
-0.5337899688 3148
 
7.9%
0.0001935690325 3121
 
7.8%
0.8011688757 3113
 
7.8%
-1.067773507 3108
 
7.8%
-1.601757044 3107
 
7.8%
-0.8007817377 3102
 
7.8%
1.602144182 3090
 
7.7%
1.335152414 3075
 
7.7%
0.5341771068 3074
 
7.7%
1.068160645 3065
 
7.7%
Other values (3) 8997
22.5%
ValueCountFrequency (%)
-1.601757044 3107
7.8%
-1.334765275 3024
7.6%
-1.067773507 3108
7.8%
-0.8007817377 3102
7.8%
-0.5337899688 3148
7.9%
-0.2667981999 2934
7.3%
0.0001935690325 3121
7.8%
0.2671853379 3039
7.6%
0.5341771068 3074
7.7%
0.8011688757 3113
7.8%
ValueCountFrequency (%)
1.602144182 3090
7.7%
1.335152414 3075
7.7%
1.068160645 3065
7.7%
0.8011688757 3113
7.8%
0.5341771068 3074
7.7%
0.2671853379 3039
7.6%
0.0001935690325 3121
7.8%
-0.2667981999 2934
7.3%
-0.5337899688 3148
7.9%
-0.8007817377 3102
7.8%

Age_Insured
Real number (ℝ)

Distinct46
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4478197 × 10-16
Minimum-2.1834358
Maximum2.7427556
Zeros0
Zeros (%)0.0%
Negative20278
Negative (%)50.7%
Memory size312.6 KiB
2025-07-25T15:20:29.991512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.1834358
5-th percentile-1.4171394
Q1-0.76031385
median-0.10348833
Q30.55333718
95-th percentile1.9764591
Maximum2.7427556
Range4.9261914
Interquartile range (IQR)1.313651

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)4.0853192 × 1015
Kurtosis-0.25047864
Mean2.4478197 × 10-16
Median Absolute Deviation (MAD)0.65682552
Skewness0.48571181
Sum9.9049657 × 10-12
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:30.074024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.4438662626 1972
 
4.9%
0.005982585735 1955
 
4.9%
0.2249244241 1785
 
4.5%
-0.5413720103 1751
 
4.4%
-0.8697847679 1712
 
4.3%
-0.9792556871 1687
 
4.2%
-0.1034883335 1679
 
4.2%
-0.2129592527 1661
 
4.2%
0.1154535049 1585
 
4.0%
-0.6508429295 1542
 
3.9%
Other values (36) 22671
56.7%
ValueCountFrequency (%)
-2.183435798 37
 
0.1%
-2.073964879 37
 
0.1%
-1.96449396 240
 
0.6%
-1.855023041 40
 
0.1%
-1.745552122 256
 
0.6%
-1.636081202 403
 
1.0%
-1.526610283 628
1.6%
-1.417139364 1014
2.5%
-1.307668445 907
2.3%
-1.198197526 1206
3.0%
ValueCountFrequency (%)
2.742755566 96
 
0.2%
2.633284647 69
 
0.2%
2.523813727 160
 
0.4%
2.414342808 432
1.1%
2.304871889 357
0.9%
2.19540097 200
 
0.5%
2.085930051 287
0.7%
1.976459131 623
1.6%
1.866988212 313
0.8%
1.757517293 588
1.5%

Policy_Num
Real number (ℝ)

Distinct1000
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.5011104 × 10-16
Minimum-1.6859792
Maximum1.7282638
Zeros0
Zeros (%)0.0%
Negative20074
Negative (%)50.2%
Memory size312.6 KiB
2025-07-25T15:20:30.152552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.6859792
5-th percentile-1.4950263
Q1-0.89678524
median-0.015259597
Q30.88247901
95-th percentile1.549921
Maximum1.7282638
Range3.414243
Interquartile range (IQR)1.7792643

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)-3.9982741 × 1015
Kurtosis-1.2910867
Mean-2.5011104 × 10-16
Median Absolute Deviation (MAD)0.891788
Skewness0.052351151
Sum-1.0587087 × 10-11
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:30.242071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.044942227 61
 
0.2%
0.9501075357 59
 
0.1%
-0.01525959687 59
 
0.1%
-0.8212480696 58
 
0.1%
-0.7519556306 57
 
0.1%
-1.231281825 56
 
0.1%
0.5823538571 56
 
0.1%
1.717427231 56
 
0.1%
1.164719135 55
 
0.1%
0.9076063777 55
 
0.1%
Other values (990) 39428
98.6%
ValueCountFrequency (%)
-1.685979241 50
0.1%
-1.681412077 46
0.1%
-1.676517176 31
0.1%
-1.675872433 46
0.1%
-1.671665276 44
0.1%
-1.661200883 39
0.1%
-1.649865821 29
0.1%
-1.649774742 40
0.1%
-1.647055306 33
0.1%
-1.644180366 43
0.1%
ValueCountFrequency (%)
1.728263785 46
0.1%
1.727598079 39
0.1%
1.723188995 30
0.1%
1.722342441 52
0.1%
1.718921952 39
0.1%
1.717427231 56
0.1%
1.711476464 38
0.1%
1.710475291 39
0.1%
1.69695811 41
0.1%
1.693607776 45
0.1%

Policy_State
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
1.1831008835068917
14126 
-1.2329932268865154
13300 
-0.0249461716898119
12574 

Length

Max length19
Median length19
Mean length18.64685
Min length18

Characters and Unicode

Total characters745874
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.1831008835068917
2nd row-1.2329932268865154
3rd row1.1831008835068917
4th row-1.2329932268865154
5th row1.1831008835068917

Common Values

ValueCountFrequency (%)
1.1831008835068917 14126
35.3%
-1.2329932268865154 13300
33.2%
-0.0249461716898119 12574
31.4%

Length

2025-07-25T15:20:30.427985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:30.487308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.1831008835068917 14126
35.3%
1.2329932268865154 13300
33.2%
0.0249461716898119 12574
31.4%

Most occurring characters

ValueCountFrequency (%)
1 133400
17.9%
8 108252
14.5%
9 78448
10.5%
0 67526
9.1%
6 65874
8.8%
2 65774
8.8%
3 54852
7.4%
5 40726
 
5.5%
. 40000
 
5.4%
4 38448
 
5.2%
Other values (2) 52574
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 745874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 133400
17.9%
8 108252
14.5%
9 78448
10.5%
0 67526
9.1%
6 65874
8.8%
2 65774
8.8%
3 54852
7.4%
5 40726
 
5.5%
. 40000
 
5.4%
4 38448
 
5.2%
Other values (2) 52574
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 745874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 133400
17.9%
8 108252
14.5%
9 78448
10.5%
0 67526
9.1%
6 65874
8.8%
2 65774
8.8%
3 54852
7.4%
5 40726
 
5.5%
. 40000
 
5.4%
4 38448
 
5.2%
Other values (2) 52574
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 745874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 133400
17.9%
8 108252
14.5%
9 78448
10.5%
0 67526
9.1%
6 65874
8.8%
2 65774
8.8%
3 54852
7.4%
5 40726
 
5.5%
. 40000
 
5.4%
4 38448
 
5.2%
Other values (2) 52574
 
7.0%
Distinct112
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
Minimum2023-07-01 00:00:00
Maximum2023-11-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-25T15:20:30.563042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:30.659217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct113
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
Minimum2024-01-01 00:00:00
Maximum2024-05-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-25T15:20:30.749480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:30.844344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Policy_Ded
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
-0.2211881611379859
14072 
-1.0394186656713662
13681 
1.4152728479287748
12247 

Length

Max length19
Median length19
Mean length18.693825
Min length18

Characters and Unicode

Total characters747753
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.2211881611379859
2nd row-1.0394186656713662
3rd row-1.0394186656713662
4th row-0.2211881611379859
5th row-0.2211881611379859

Common Values

ValueCountFrequency (%)
-0.2211881611379859 14072
35.2%
-1.0394186656713662 13681
34.2%
1.4152728479287748 12247
30.6%

Length

2025-07-25T15:20:30.924860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:30.972316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.2211881611379859 14072
35.2%
1.0394186656713662 13681
34.2%
1.4152728479287748 12247
30.6%

Most occurring characters

ValueCountFrequency (%)
1 135897
18.2%
8 92638
12.4%
6 82477
11.0%
2 78566
10.5%
7 76741
10.3%
9 54072
 
7.2%
4 50422
 
6.7%
3 41434
 
5.5%
. 40000
 
5.3%
5 40000
 
5.3%
Other values (2) 55506
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 747753
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 135897
18.2%
8 92638
12.4%
6 82477
11.0%
2 78566
10.5%
7 76741
10.3%
9 54072
 
7.2%
4 50422
 
6.7%
3 41434
 
5.5%
. 40000
 
5.3%
5 40000
 
5.3%
Other values (2) 55506
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 747753
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 135897
18.2%
8 92638
12.4%
6 82477
11.0%
2 78566
10.5%
7 76741
10.3%
9 54072
 
7.2%
4 50422
 
6.7%
3 41434
 
5.5%
. 40000
 
5.3%
5 40000
 
5.3%
Other values (2) 55506
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 747753
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 135897
18.2%
8 92638
12.4%
6 82477
11.0%
2 78566
10.5%
7 76741
10.3%
9 54072
 
7.2%
4 50422
 
6.7%
3 41434
 
5.5%
. 40000
 
5.3%
5 40000
 
5.3%
Other values (2) 55506
7.4%

Policy_Premium
Real number (ℝ)

Distinct991
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.907985 × 10-16
Minimum-3.3483967
Maximum3.2221613
Zeros0
Zeros (%)0.0%
Negative19765
Negative (%)49.4%
Memory size312.6 KiB
2025-07-25T15:20:31.040020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.3483967
5-th percentile-1.6645045
Q1-0.69096487
median0.012427475
Q30.66156243
95-th percentile1.6274091
Maximum3.2221613
Range6.570558
Interquartile range (IQR)1.3525273

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)2.5588954 × 1015
Kurtosis0.038683109
Mean3.907985 × 10-16
Median Absolute Deviation (MAD)0.66883532
Skewness-0.0096481834
Sum1.5418777 × 10-11
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:31.122539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4813285722 96
 
0.2%
-0.7403785843 93
 
0.2%
1.09281305 87
 
0.2%
1.230552748 85
 
0.2%
-0.7413554616 83
 
0.2%
0.1029107333 72
 
0.2%
0.4351304172 72
 
0.2%
0.5420170736 70
 
0.2%
-0.1652827851 67
 
0.2%
-1.019602675 61
 
0.2%
Other values (981) 39214
98.0%
ValueCountFrequency (%)
-3.34839672 39
0.1%
-3.139426387 36
0.1%
-2.921664159 42
0.1%
-2.807939361 50
0.1%
-2.600352938 49
0.1%
-2.567912471 43
0.1%
-2.451582668 41
0.1%
-2.405995061 39
0.1%
-2.380962581 40
0.1%
-2.377258588 49
0.1%
ValueCountFrequency (%)
3.222161316 39
0.1%
2.904839011 45
0.1%
2.767343532 43
0.1%
2.734862363 29
0.1%
2.714388643 42
0.1%
2.608845193 40
0.1%
2.533666345 41
0.1%
2.482339584 39
0.1%
2.470983386 45
0.1%
2.464430167 44
0.1%

Umbrella_Limit
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7022828 × 10-17
Minimum-0.47963309
Maximum3.9013779
Zeros0
Zeros (%)0.0%
Negative32002
Negative (%)80.0%
Memory size312.6 KiB
2025-07-25T15:20:31.187546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.47963309
5-th percentile-0.47963309
Q1-0.47963309
median-0.47963309
Q3-0.47963309
95-th percentile2.1489735
Maximum3.9013779
Range4.381011
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)3.7006211 × 1016
Kurtosis1.7558994
Mean2.7022828 × 10-17
Median Absolute Deviation (MAD)0
Skewness1.8032284
Sum2.8510527 × 10-13
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:31.246062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
-0.4796330874 31960
79.9%
2.14897353 2224
 
5.6%
1.710872427 1877
 
4.7%
1.272771324 1588
 
4.0%
2.587074633 1183
 
3.0%
0.8346702212 443
 
1.1%
3.025175735 321
 
0.8%
3.463276838 177
 
0.4%
0.3965691183 119
 
0.3%
3.901377941 66
 
0.2%
ValueCountFrequency (%)
-0.4796330874 31960
79.9%
-0.04153198455 42
 
0.1%
0.3965691183 119
 
0.3%
0.8346702212 443
 
1.1%
1.272771324 1588
 
4.0%
1.710872427 1877
 
4.7%
2.14897353 2224
 
5.6%
2.587074633 1183
 
3.0%
3.025175735 321
 
0.8%
3.463276838 177
 
0.4%
ValueCountFrequency (%)
3.901377941 66
 
0.2%
3.463276838 177
 
0.4%
3.025175735 321
 
0.8%
2.587074633 1183
3.0%
2.14897353 2224
5.6%
1.710872427 1877
4.7%
1.272771324 1588
4.0%
0.8346702212 443
 
1.1%
0.3965691183 119
 
0.3%
-0.04153198455 42
 
0.1%

Insured_Zip
Real number (ℝ)

Distinct995
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.5063506 × 10-16
Minimum-0.99442167
Maximum1.6670296
Zeros0
Zeros (%)0.0%
Negative28196
Negative (%)70.5%
Memory size312.6 KiB
2025-07-25T15:20:31.319104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.99442167
5-th percentile-0.95020313
Q1-0.73878804
median-0.48691946
Q31.4200556
95-th percentile1.6182421
Maximum1.6670296
Range2.6614513
Interquartile range (IQR)2.1588436

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)-6.6386438 × 1015
Kurtosis-1.2001256
Mean-1.5063506 × 10-16
Median Absolute Deviation (MAD)0.305946
Skewness0.80953764
Sum-5.7251981 × 10-12
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:31.406146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.3307810236 92
 
0.2%
-0.7602767694 84
 
0.2%
-0.9791104297 80
 
0.2%
-0.6249158862 71
 
0.2%
-0.4460476416 69
 
0.2%
-0.8303347768 61
 
0.2%
1.384510639 59
 
0.1%
-0.3367353965 59
 
0.1%
-0.9298718103 58
 
0.1%
1.647618617 57
 
0.1%
Other values (985) 39310
98.3%
ValueCountFrequency (%)
-0.9944216742 40
0.1%
-0.9939057215 47
0.1%
-0.9926367568 40
0.1%
-0.9905729461 43
0.1%
-0.9879652933 43
0.1%
-0.9872122813 39
0.1%
-0.98705889 49
0.1%
-0.9865987159 37
0.1%
-0.9859154273 54
0.1%
-0.9842699565 40
0.1%
ValueCountFrequency (%)
1.667029593 46
0.1%
1.665732739 37
0.1%
1.665035506 48
0.1%
1.664170937 42
0.1%
1.663892043 38
0.1%
1.66068477 54
0.1%
1.660489545 28
0.1%
1.660210651 46
0.1%
1.658607015 34
0.1%
1.65650137 34
0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
female
21614 
male
18386 

Length

Max length6
Median length6
Mean length5.0807
Min length4

Characters and Unicode

Total characters203228
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowmale
3rd rowfemale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
female 21614
54.0%
male 18386
46.0%

Length

2025-07-25T15:20:31.485157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:31.531202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 21614
54.0%
male 18386
46.0%

Most occurring characters

ValueCountFrequency (%)
e 61614
30.3%
a 40000
19.7%
m 40000
19.7%
l 40000
19.7%
f 21614
 
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 61614
30.3%
a 40000
19.7%
m 40000
19.7%
l 40000
19.7%
f 21614
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 61614
30.3%
a 40000
19.7%
m 40000
19.7%
l 40000
19.7%
f 21614
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 61614
30.3%
a 40000
19.7%
m 40000
19.7%
l 40000
19.7%
f 21614
 
10.6%

Education
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
jd
6443 
high school
6405 
associate
5801 
masters
5721 
md
5713 
Other values (2)
9917 

Length

Max length11
Median length9
Mean length5.91825
Min length2

Characters and Unicode

Total characters236730
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhigh school
2nd rowassociate
3rd rowmasters
4th rowphd
5th rowmd

Common Values

ValueCountFrequency (%)
jd 6443
16.1%
high school 6405
16.0%
associate 5801
14.5%
masters 5721
14.3%
md 5713
14.3%
college 4989
12.5%
phd 4928
12.3%

Length

2025-07-25T15:20:31.588211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:31.651146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
jd 6443
13.9%
high 6405
13.8%
school 6405
13.8%
associate 5801
12.5%
masters 5721
12.3%
md 5713
12.3%
college 4989
10.8%
phd 4928
10.6%

Most occurring characters

ValueCountFrequency (%)
s 29449
12.4%
h 24143
10.2%
o 23600
10.0%
e 21500
9.1%
a 17323
 
7.3%
c 17195
 
7.3%
d 17084
 
7.2%
l 16383
 
6.9%
i 12206
 
5.2%
t 11522
 
4.9%
Other values (6) 46325
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 236730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 29449
12.4%
h 24143
10.2%
o 23600
10.0%
e 21500
9.1%
a 17323
 
7.3%
c 17195
 
7.3%
d 17084
 
7.2%
l 16383
 
6.9%
i 12206
 
5.2%
t 11522
 
4.9%
Other values (6) 46325
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 236730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 29449
12.4%
h 24143
10.2%
o 23600
10.0%
e 21500
9.1%
a 17323
 
7.3%
c 17195
 
7.3%
d 17084
 
7.2%
l 16383
 
6.9%
i 12206
 
5.2%
t 11522
 
4.9%
Other values (6) 46325
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 236730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 29449
12.4%
h 24143
10.2%
o 23600
10.0%
e 21500
9.1%
a 17323
 
7.3%
c 17195
 
7.3%
d 17084
 
7.2%
l 16383
 
6.9%
i 12206
 
5.2%
t 11522
 
4.9%
Other values (6) 46325
19.6%

Occupation
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
machine-op-inspct
3763 
prof-specialty
3391 
craft-repair
3080 
sales
3010 
tech-support
2988 
Other values (9)
23768 

Length

Max length17
Median length16
Mean length13.533475
Min length5

Characters and Unicode

Total characters541339
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadm-clerical
2nd rowprotective-serv
3rd rowpriv-house-serv
4th rowtech-support
5th rowexec-managerial

Common Values

ValueCountFrequency (%)
machine-op-inspct 3763
 
9.4%
prof-specialty 3391
 
8.5%
craft-repair 3080
 
7.7%
sales 3010
 
7.5%
tech-support 2988
 
7.5%
exec-managerial 2970
 
7.4%
priv-house-serv 2869
 
7.2%
other-service 2864
 
7.2%
transport-moving 2859
 
7.1%
armed-forces 2761
 
6.9%
Other values (4) 9445
23.6%

Length

2025-07-25T15:20:31.743193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
machine-op-inspct 3763
 
9.4%
prof-specialty 3391
 
8.5%
craft-repair 3080
 
7.7%
sales 3010
 
7.5%
tech-support 2988
 
7.5%
exec-managerial 2970
 
7.4%
priv-house-serv 2869
 
7.2%
other-service 2864
 
7.2%
transport-moving 2859
 
7.1%
armed-forces 2761
 
6.9%
Other values (4) 9445
23.6%

Most occurring characters

ValueCountFrequency (%)
e 61708
11.4%
r 55460
10.2%
- 43622
 
8.1%
a 42455
 
7.8%
s 39436
 
7.3%
i 37185
 
6.9%
c 35408
 
6.5%
p 31651
 
5.8%
t 29910
 
5.5%
o 26913
 
5.0%
Other values (11) 137591
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 541339
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 61708
11.4%
r 55460
10.2%
- 43622
 
8.1%
a 42455
 
7.8%
s 39436
 
7.3%
i 37185
 
6.9%
c 35408
 
6.5%
p 31651
 
5.8%
t 29910
 
5.5%
o 26913
 
5.0%
Other values (11) 137591
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 541339
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 61708
11.4%
r 55460
10.2%
- 43622
 
8.1%
a 42455
 
7.8%
s 39436
 
7.3%
i 37185
 
6.9%
c 35408
 
6.5%
p 31651
 
5.8%
t 29910
 
5.5%
o 26913
 
5.0%
Other values (11) 137591
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 541339
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 61708
11.4%
r 55460
10.2%
- 43622
 
8.1%
a 42455
 
7.8%
s 39436
 
7.3%
i 37185
 
6.9%
c 35408
 
6.5%
p 31651
 
5.8%
t 29910
 
5.5%
o 26913
 
5.0%
Other values (11) 137591
25.4%

Hobbies
Categorical

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
reading
 
2619
exercise
 
2282
bungie-jumping
 
2263
paintball
 
2252
movies
 
2237
Other values (15)
28347 

Length

Max length14
Median length11
Mean length8.110075
Min length4

Characters and Unicode

Total characters324403
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpolo
2nd rowmovies
3rd rowboard-games
4th rowreading
5th rowcamping

Common Values

ValueCountFrequency (%)
reading 2619
 
6.5%
exercise 2282
 
5.7%
bungie-jumping 2263
 
5.7%
paintball 2252
 
5.6%
movies 2237
 
5.6%
camping 2207
 
5.5%
kayaking 2196
 
5.5%
golf 2141
 
5.4%
yachting 2106
 
5.3%
hiking 2054
 
5.1%
Other values (10) 17643
44.1%

Length

2025-07-25T15:20:31.909242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
reading 2619
 
6.5%
exercise 2282
 
5.7%
bungie-jumping 2263
 
5.7%
paintball 2252
 
5.6%
movies 2237
 
5.6%
camping 2207
 
5.5%
kayaking 2196
 
5.5%
golf 2141
 
5.4%
yachting 2106
 
5.3%
hiking 2054
 
5.1%
Other values (10) 17643
44.1%

Most occurring characters

ValueCountFrequency (%)
i 37129
 
11.4%
g 28980
 
8.9%
e 28269
 
8.7%
a 27900
 
8.6%
n 26778
 
8.3%
s 21843
 
6.7%
o 13577
 
4.2%
l 12828
 
4.0%
m 12580
 
3.9%
p 12193
 
3.8%
Other values (14) 102326
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 324403
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 37129
 
11.4%
g 28980
 
8.9%
e 28269
 
8.7%
a 27900
 
8.6%
n 26778
 
8.3%
s 21843
 
6.7%
o 13577
 
4.2%
l 12828
 
4.0%
m 12580
 
3.9%
p 12193
 
3.8%
Other values (14) 102326
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 324403
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 37129
 
11.4%
g 28980
 
8.9%
e 28269
 
8.7%
a 27900
 
8.6%
n 26778
 
8.3%
s 21843
 
6.7%
o 13577
 
4.2%
l 12828
 
4.0%
m 12580
 
3.9%
p 12193
 
3.8%
Other values (14) 102326
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 324403
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 37129
 
11.4%
g 28980
 
8.9%
e 28269
 
8.7%
a 27900
 
8.6%
n 26778
 
8.3%
s 21843
 
6.7%
o 13577
 
4.2%
l 12828
 
4.0%
m 12580
 
3.9%
p 12193
 
3.8%
Other values (14) 102326
31.5%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
own-child
7343 
other-relative
7053 
not-in-family
6978 
husband
6758 
wife
6176 

Length

Max length14
Median length13
Mean length9.469525
Min length4

Characters and Unicode

Total characters378781
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowother-relative
2nd rowhusband
3rd rowother-relative
4th rowown-child
5th rowhusband

Common Values

ValueCountFrequency (%)
own-child 7343
18.4%
other-relative 7053
17.6%
not-in-family 6978
17.4%
husband 6758
16.9%
wife 6176
15.4%
unmarried 5692
14.2%

Length

2025-07-25T15:20:31.979302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:32.040040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
own-child 7343
18.4%
other-relative 7053
17.6%
not-in-family 6978
17.4%
husband 6758
16.9%
wife 6176
15.4%
unmarried 5692
14.2%

Most occurring characters

ValueCountFrequency (%)
i 40220
 
10.6%
n 33749
 
8.9%
e 33027
 
8.7%
- 28352
 
7.5%
a 26481
 
7.0%
r 25490
 
6.7%
l 21374
 
5.6%
o 21374
 
5.6%
h 21154
 
5.6%
t 21084
 
5.6%
Other values (10) 106476
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 378781
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 40220
 
10.6%
n 33749
 
8.9%
e 33027
 
8.7%
- 28352
 
7.5%
a 26481
 
7.0%
r 25490
 
6.7%
l 21374
 
5.6%
o 21374
 
5.6%
h 21154
 
5.6%
t 21084
 
5.6%
Other values (10) 106476
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 378781
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 40220
 
10.6%
n 33749
 
8.9%
e 33027
 
8.7%
- 28352
 
7.5%
a 26481
 
7.0%
r 25490
 
6.7%
l 21374
 
5.6%
o 21374
 
5.6%
h 21154
 
5.6%
t 21084
 
5.6%
Other values (10) 106476
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 378781
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 40220
 
10.6%
n 33749
 
8.9%
e 33027
 
8.7%
- 28352
 
7.5%
a 26481
 
7.0%
r 25490
 
6.7%
l 21374
 
5.6%
o 21374
 
5.6%
h 21154
 
5.6%
t 21084
 
5.6%
Other values (10) 106476
28.1%

Capital_Gains
Real number (ℝ)

Distinct338
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5916157 × 10-16
Minimum-0.90469909
Maximum2.7157281
Zeros0
Zeros (%)0.0%
Negative21240
Negative (%)53.1%
Memory size312.6 KiB
2025-07-25T15:20:32.129567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.90469909
5-th percentile-0.90469909
Q1-0.90469909
median-0.90469909
Q30.92532778
95-th percentile1.638606
Maximum2.7157281
Range3.6204272
Interquartile range (IQR)1.8300269

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)6.2830021 × 1015
Kurtosis-1.2805004
Mean1.5916157 × 10-16
Median Absolute Deviation (MAD)0
Skewness0.47275984
Sum1.3848478 × 10-11
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:32.215709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.9046990932 20249
50.6%
0.763219103 206
 
0.5%
1.562955258 183
 
0.5%
0.6803635555 140
 
0.4%
0.7343997821 140
 
0.4%
0.9505446888 138
 
0.3%
0.7776287635 133
 
0.3%
1.242340313 131
 
0.3%
0.4245920826 129
 
0.3%
1.202713747 127
 
0.3%
Other values (328) 18424
46.1%
ValueCountFrequency (%)
-0.9046990932 20249
50.6%
-0.8758797724 41
 
0.1%
-0.5444575822 46
 
0.1%
-0.5084334311 36
 
0.1%
-0.4688068648 46
 
0.1%
-0.4435899591 43
 
0.1%
-0.4327827137 33
 
0.1%
-0.3967585626 47
 
0.1%
-0.3247102604 48
 
0.1%
-0.2814812791 40
 
0.1%
ValueCountFrequency (%)
2.715728093 33
0.1%
2.654487036 45
0.1%
2.510390432 44
0.1%
2.405920394 29
0.1%
2.362691412 39
0.1%
2.294245525 36
0.1%
2.279835865 34
0.1%
2.258221374 35
0.1%
2.153751336 36
0.1%
2.117727185 45
0.1%

Capital_Loss
Real number (ℝ)

Distinct354
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1832315 × 10-16
Minimum-3.0028522
Maximum0.95250361
Zeros0
Zeros (%)0.0%
Negative19716
Negative (%)49.3%
Memory size312.6 KiB
2025-07-25T15:20:32.302229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.0028522
5-th percentile-1.6143834
Q1-0.88098715
median0.15502414
Q30.95250361
95-th percentile0.95250361
Maximum0.95250361
Range3.9553558
Interquartile range (IQR)1.8334908

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)3.1415011 × 1015
Kurtosis-1.3114989
Mean3.1832315 × 10-16
Median Absolute Deviation (MAD)0.79747947
Skewness-0.39283306
Sum3.9719339 × 10-12
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:32.397172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9525036105 19043
47.6%
-0.1760722535 217
 
0.5%
-0.9593110234 191
 
0.5%
-0.8382650317 181
 
0.5%
-0.7991030932 176
 
0.4%
-0.215234192 174
 
0.4%
-0.9628711997 169
 
0.4%
-1.233444593 161
 
0.4%
-0.8631862653 153
 
0.4%
-1.126639306 151
 
0.4%
Other values (344) 19384
48.5%
ValueCountFrequency (%)
-3.002852178 44
0.1%
-2.379821338 45
0.1%
-2.301497461 29
0.1%
-2.294377109 42
0.1%
-2.273016051 48
0.1%
-2.258775346 30
0.1%
-2.25521517 34
0.1%
-2.230293937 37
0.1%
-2.191131998 41
0.1%
-2.155530236 36
0.1%
ValueCountFrequency (%)
0.9525036105 19043
47.6%
0.7495735655 26
 
0.1%
0.7282125082 36
 
0.1%
0.6498886312 48
 
0.1%
0.5751249304 42
 
0.1%
0.521722287 29
 
0.1%
0.4825603485 38
 
0.1%
0.4611992912 48
 
0.1%
0.3971161191 36
 
0.1%
0.3935559428 76
 
0.2%

Garage_Location
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
False
39758 
True
 
242
ValueCountFrequency (%)
False 39758
99.4%
True 242
 
0.6%
2025-07-25T15:20:32.458872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
Minimum2024-01-01 00:00:00
Maximum2024-03-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-25T15:20:32.519723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:32.618199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Accident_Type
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
multi-vehicle collision
16817 
single vehicle collision
16110 
vehicle theft
3710 
parked car
3363 

Length

Max length24
Median length23
Mean length21.382275
Min length10

Characters and Unicode

Total characters855291
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsingle vehicle collision
2nd rowmulti-vehicle collision
3rd rowmulti-vehicle collision
4th rowsingle vehicle collision
5th rowmulti-vehicle collision

Common Values

ValueCountFrequency (%)
multi-vehicle collision 16817
42.0%
single vehicle collision 16110
40.3%
vehicle theft 3710
 
9.3%
parked car 3363
 
8.4%

Length

2025-07-25T15:20:32.703653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:32.757938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
collision 32927
34.3%
vehicle 19820
20.6%
multi-vehicle 16817
17.5%
single 16110
16.8%
theft 3710
 
3.9%
parked 3363
 
3.5%
car 3363
 
3.5%

Most occurring characters

ValueCountFrequency (%)
l 135418
15.8%
i 135418
15.8%
e 96457
11.3%
c 72927
8.5%
o 65854
7.7%
56110
6.6%
s 49037
 
5.7%
n 49037
 
5.7%
h 40347
 
4.7%
v 36637
 
4.3%
Other values (11) 118049
13.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 855291
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 135418
15.8%
i 135418
15.8%
e 96457
11.3%
c 72927
8.5%
o 65854
7.7%
56110
6.6%
s 49037
 
5.7%
n 49037
 
5.7%
h 40347
 
4.7%
v 36637
 
4.3%
Other values (11) 118049
13.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 855291
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 135418
15.8%
i 135418
15.8%
e 96457
11.3%
c 72927
8.5%
o 65854
7.7%
56110
6.6%
s 49037
 
5.7%
n 49037
 
5.7%
h 40347
 
4.7%
v 36637
 
4.3%
Other values (11) 118049
13.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 855291
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 135418
15.8%
i 135418
15.8%
e 96457
11.3%
c 72927
8.5%
o 65854
7.7%
56110
6.6%
s 49037
 
5.7%
n 49037
 
5.7%
h 40347
 
4.7%
v 36637
 
4.3%
Other values (11) 118049
13.8%

Collision_Type
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
rear collision
11728 
side collision
11047 
front collision
10152 
unknown
7073 

Length

Max length15
Median length14
Mean length13.016025
Min length7

Characters and Unicode

Total characters520641
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrear collision
2nd rowside collision
3rd rowrear collision
4th rowrear collision
5th rowside collision

Common Values

ValueCountFrequency (%)
rear collision 11728
29.3%
side collision 11047
27.6%
front collision 10152
25.4%
unknown 7073
17.7%

Length

2025-07-25T15:20:32.834542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:32.888879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
collision 32927
45.2%
rear 11728
 
16.1%
side 11047
 
15.1%
front 10152
 
13.9%
unknown 7073
 
9.7%

Most occurring characters

ValueCountFrequency (%)
o 83079
16.0%
i 76901
14.8%
l 65854
12.6%
n 64298
12.3%
s 43974
8.4%
r 33608
6.5%
32927
 
6.3%
c 32927
 
6.3%
e 22775
 
4.4%
a 11728
 
2.3%
Other values (6) 52570
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 520641
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 83079
16.0%
i 76901
14.8%
l 65854
12.6%
n 64298
12.3%
s 43974
8.4%
r 33608
6.5%
32927
 
6.3%
c 32927
 
6.3%
e 22775
 
4.4%
a 11728
 
2.3%
Other values (6) 52570
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 520641
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 83079
16.0%
i 76901
14.8%
l 65854
12.6%
n 64298
12.3%
s 43974
8.4%
r 33608
6.5%
32927
 
6.3%
c 32927
 
6.3%
e 22775
 
4.4%
a 11728
 
2.3%
Other values (6) 52570
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 520641
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 83079
16.0%
i 76901
14.8%
l 65854
12.6%
n 64298
12.3%
s 43974
8.4%
r 33608
6.5%
32927
 
6.3%
c 32927
 
6.3%
e 22775
 
4.4%
a 11728
 
2.3%
Other values (6) 52570
10.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
minor damage
14222 
total loss
11187 
major damage
11052 
trivial damage
3539 

Length

Max length14
Median length12
Mean length11.6176
Min length10

Characters and Unicode

Total characters464704
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtotal loss
2nd rowmajor damage
3rd rowtotal loss
4th rowmajor damage
5th rowtotal loss

Common Values

ValueCountFrequency (%)
minor damage 14222
35.6%
total loss 11187
28.0%
major damage 11052
27.6%
trivial damage 3539
 
8.8%

Length

2025-07-25T15:20:32.963024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:33.018669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
damage 28813
36.0%
minor 14222
17.8%
total 11187
 
14.0%
loss 11187
 
14.0%
major 11052
 
13.8%
trivial 3539
 
4.4%

Most occurring characters

ValueCountFrequency (%)
a 83404
17.9%
m 54087
11.6%
o 47648
10.3%
40000
8.6%
r 28813
 
6.2%
g 28813
 
6.2%
d 28813
 
6.2%
e 28813
 
6.2%
t 25913
 
5.6%
l 25913
 
5.6%
Other values (5) 72487
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 464704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 83404
17.9%
m 54087
11.6%
o 47648
10.3%
40000
8.6%
r 28813
 
6.2%
g 28813
 
6.2%
d 28813
 
6.2%
e 28813
 
6.2%
t 25913
 
5.6%
l 25913
 
5.6%
Other values (5) 72487
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 464704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 83404
17.9%
m 54087
11.6%
o 47648
10.3%
40000
8.6%
r 28813
 
6.2%
g 28813
 
6.2%
d 28813
 
6.2%
e 28813
 
6.2%
t 25913
 
5.6%
l 25913
 
5.6%
Other values (5) 72487
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 464704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 83404
17.9%
m 54087
11.6%
o 47648
10.3%
40000
8.6%
r 28813
 
6.2%
g 28813
 
6.2%
d 28813
 
6.2%
e 28813
 
6.2%
t 25913
 
5.6%
l 25913
 
5.6%
Other values (5) 72487
15.6%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
police
11491 
fire
8801 
other
8102 
ambulance
8006 
unknown
3600 

Length

Max length9
Median length7
Mean length6.04785
Min length4

Characters and Unicode

Total characters241914
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowambulance
2nd rowother
3rd rowpolice
4th rowambulance
5th rowother

Common Values

ValueCountFrequency (%)
police 11491
28.7%
fire 8801
22.0%
other 8102
20.3%
ambulance 8006
20.0%
unknown 3600
 
9.0%

Length

2025-07-25T15:20:33.088083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:33.142593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
police 11491
28.7%
fire 8801
22.0%
other 8102
20.3%
ambulance 8006
20.0%
unknown 3600
 
9.0%

Most occurring characters

ValueCountFrequency (%)
e 36400
15.0%
o 23193
9.6%
i 20292
8.4%
c 19497
8.1%
l 19497
8.1%
n 18806
 
7.8%
r 16903
 
7.0%
a 16012
 
6.6%
u 11606
 
4.8%
p 11491
 
4.8%
Other values (7) 48217
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 241914
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 36400
15.0%
o 23193
9.6%
i 20292
8.4%
c 19497
8.1%
l 19497
8.1%
n 18806
 
7.8%
r 16903
 
7.0%
a 16012
 
6.6%
u 11606
 
4.8%
p 11491
 
4.8%
Other values (7) 48217
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 241914
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 36400
15.0%
o 23193
9.6%
i 20292
8.4%
c 19497
8.1%
l 19497
8.1%
n 18806
 
7.8%
r 16903
 
7.0%
a 16012
 
6.6%
u 11606
 
4.8%
p 11491
 
4.8%
Other values (7) 48217
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 241914
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 36400
15.0%
o 23193
9.6%
i 20292
8.4%
c 19497
8.1%
l 19497
8.1%
n 18806
 
7.8%
r 16903
 
7.0%
a 16012
 
6.6%
u 11606
 
4.8%
p 11491
 
4.8%
Other values (7) 48217
19.9%

Acccident_State
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.5579538 × 10-17
Minimum-1.5063043
Maximum1.2771345
Zeros0
Zeros (%)0.0%
Negative16969
Negative (%)42.4%
Memory size312.6 KiB
2025-07-25T15:20:33.198265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.5063043
5-th percentile-1.5063043
Q1-1.0423979
median0.34932158
Q30.81322806
95-th percentile1.2771345
Maximum1.2771345
Range2.7834389
Interquartile range (IQR)1.8556259

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)-3.9094235 × 1016
Kurtosis-1.4920915
Mean-2.5579538 × 10-17
Median Absolute Deviation (MAD)0.92781296
Skewness-0.15088727
Sum-1.0231815 × 10-12
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:33.247265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-1.042397863 10493
26.2%
0.3493215802 9913
24.8%
1.277134542 8736
21.8%
0.8132280613 4382
11.0%
-1.506304344 4375
10.9%
-0.1145849008 1207
 
3.0%
-0.5784913819 894
 
2.2%
ValueCountFrequency (%)
-1.506304344 4375
10.9%
-1.042397863 10493
26.2%
-0.5784913819 894
 
2.2%
-0.1145849008 1207
 
3.0%
0.3493215802 9913
24.8%
0.8132280613 4382
11.0%
1.277134542 8736
21.8%
ValueCountFrequency (%)
1.277134542 8736
21.8%
0.8132280613 4382
11.0%
0.3493215802 9913
24.8%
-0.1145849008 1207
 
3.0%
-0.5784913819 894
 
2.2%
-1.042397863 10493
26.2%
-1.506304344 4375
10.9%

Acccident_City
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.094947 × 10-17
Minimum-1.4785942
Maximum1.4713423
Zeros0
Zeros (%)0.0%
Negative23194
Negative (%)58.0%
Memory size312.6 KiB
2025-07-25T15:20:33.390840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.4785942
5-th percentile-1.4785942
Q1-0.98693811
median-0.0036259635
Q30.97968618
95-th percentile1.4713423
Maximum1.4713423
Range2.9499364
Interquartile range (IQR)1.9666243

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)-1.0995254 × 1016
Kurtosis-1.2832769
Mean-9.094947 × 10-17
Median Absolute Deviation (MAD)0.98331214
Skewness0.023995321
Sum-3.6379788 × 10-12
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:33.441343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1.471342251 6343
15.9%
-0.9869381067 5953
14.9%
-0.003625963528 5928
14.8%
-1.478594178 5767
14.4%
0.9796861796 5556
13.9%
-0.4952820351 5546
13.9%
0.488030108 4907
12.3%
ValueCountFrequency (%)
-1.478594178 5767
14.4%
-0.9869381067 5953
14.9%
-0.4952820351 5546
13.9%
-0.003625963528 5928
14.8%
0.488030108 4907
12.3%
0.9796861796 5556
13.9%
1.471342251 6343
15.9%
ValueCountFrequency (%)
1.471342251 6343
15.9%
0.9796861796 5556
13.9%
0.488030108 4907
12.3%
-0.003625963528 5928
14.8%
-0.4952820351 5546
13.9%
-0.9869381067 5953
14.9%
-1.478594178 5767
14.4%

Accident_Location
Real number (ℝ)

Distinct1000
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9790393 × 10-17
Minimum-1.7211443
Maximum1.7414284
Zeros0
Zeros (%)0.0%
Negative20047
Negative (%)50.1%
Memory size312.6 KiB
2025-07-25T15:20:33.512878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.7211443
5-th percentile-1.5547744
Q1-0.86503272
median-0.0054551118
Q30.86105458
95-th percentile1.5646604
Maximum1.7414284
Range3.4625727
Interquartile range (IQR)1.7260873

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)2.5132009 × 1016
Kurtosis-1.1965714
Mean3.9790393 × 10-17
Median Absolute Deviation (MAD)0.86304365
Skewness0.010409536
Sum2.0250468 × 10-12
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:33.594872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3654110354 61
 
0.2%
-1.170044135 59
 
0.1%
-1.506249894 59
 
0.1%
-1.159646018 58
 
0.1%
0.07079774094 57
 
0.1%
-0.618943972 56
 
0.1%
-1.346812111 56
 
0.1%
-1.603298979 56
 
0.1%
1.35669812 55
 
0.1%
-0.3693891814 55
 
0.1%
Other values (990) 39428
98.6%
ValueCountFrequency (%)
-1.721144297 47
0.1%
-1.717678259 48
0.1%
-1.71421222 42
0.1%
-1.710746181 40
0.1%
-1.707280142 35
0.1%
-1.703814103 41
0.1%
-1.700348065 45
0.1%
-1.696882026 39
0.1%
-1.693415987 43
0.1%
-1.689949948 45
0.1%
ValueCountFrequency (%)
1.741428423 51
0.1%
1.737962384 36
0.1%
1.734496345 45
0.1%
1.731030306 38
0.1%
1.727564268 37
0.1%
1.724098229 29
0.1%
1.72063219 34
0.1%
1.717166151 33
0.1%
1.713700113 39
0.1%
1.710234074 46
0.1%

Accident_Hour
Real number (ℝ)

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3642421 × 10-16
Minimum-1.6737861
Maximum1.6348876
Zeros0
Zeros (%)0.0%
Negative19334
Negative (%)48.3%
Memory size312.6 KiB
2025-07-25T15:20:33.666747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.6737861
5-th percentile-1.6737861
Q1-0.81065383
median0.052478442
Q30.77175534
95-th percentile1.6348876
Maximum1.6348876
Range3.3086737
Interquartile range (IQR)1.5824092

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)7.3301691 × 1015
Kurtosis-1.1989171
Mean1.3642421 × 10-16
Median Absolute Deviation (MAD)0.86313228
Skewness-0.034852126
Sum6.1390892 × 10-12
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:33.728793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
-1.242219973 2228
 
5.6%
0.7717553402 2164
 
5.4%
-1.673786112 2044
 
5.1%
1.634887617 2007
 
5.0%
0.6278999606 1944
 
4.9%
-0.2352323166 1863
 
4.7%
-1.098364594 1861
 
4.7%
0.196333822 1837
 
4.6%
-0.8106538348 1761
 
4.4%
-0.3790876962 1680
 
4.2%
Other values (14) 20611
51.5%
ValueCountFrequency (%)
-1.673786112 2044
5.1%
-1.529930733 1175
2.9%
-1.386075353 1238
3.1%
-1.242219973 2228
5.6%
-1.098364594 1861
4.7%
-0.9545092143 1290
3.2%
-0.8106538348 1761
4.4%
-0.6667984553 1554
3.9%
-0.5229430757 1409
3.5%
-0.3790876962 1680
4.2%
ValueCountFrequency (%)
1.634887617 2007
5.0%
1.491032238 1532
3.8%
1.347176858 1664
4.2%
1.203321479 1407
3.5%
1.059466099 1587
4.0%
0.9156107197 1669
4.2%
0.7717553402 2164
5.4%
0.6278999606 1944
4.9%
0.4840445811 1591
4.0%
0.3401892015 1611
4.0%

Num_of_Vehicles_Involved
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
-0.8256105836925834
23183 
1.1383169443442478
14314 
0.1563531803258321
 
1253
2.120280708362664
 
1250

Length

Max length19
Median length19
Mean length18.548325
Min length17

Characters and Unicode

Total characters741933
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.8256105836925834
2nd row2.120280708362664
3rd row1.1383169443442478
4th row-0.8256105836925834
5th row1.1383169443442478

Common Values

ValueCountFrequency (%)
-0.8256105836925834 23183
58.0%
1.1383169443442478 14314
35.8%
0.1563531803258321 1253
 
3.1%
2.120280708362664 1250
 
3.1%

Length

2025-07-25T15:20:33.803072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:33.857031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.8256105836925834 23183
58.0%
1.1383169443442478 14314
35.8%
0.1563531803258321 1253
 
3.1%
2.120280708362664 1250
 
3.1%

Most occurring characters

ValueCountFrequency (%)
8 103183
13.9%
4 96003
12.9%
3 95570
12.9%
5 73308
9.9%
1 71134
9.6%
2 68186
9.2%
6 65683
8.9%
0 52622
7.1%
. 40000
 
5.4%
9 37497
 
5.1%
Other values (2) 38747
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 741933
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 103183
13.9%
4 96003
12.9%
3 95570
12.9%
5 73308
9.9%
1 71134
9.6%
2 68186
9.2%
6 65683
8.9%
0 52622
7.1%
. 40000
 
5.4%
9 37497
 
5.1%
Other values (2) 38747
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 741933
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 103183
13.9%
4 96003
12.9%
3 95570
12.9%
5 73308
9.9%
1 71134
9.6%
2 68186
9.2%
6 65683
8.9%
0 52622
7.1%
. 40000
 
5.4%
9 37497
 
5.1%
Other values (2) 38747
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 741933
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 103183
13.9%
4 96003
12.9%
3 95570
12.9%
5 73308
9.9%
1 71134
9.6%
2 68186
9.2%
6 65683
8.9%
0 52622
7.1%
. 40000
 
5.4%
9 37497
 
5.1%
Other values (2) 38747
 
5.2%

Property_Damage
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
unknown
14477 
no
13482 
yes
12041 

Length

Max length7
Median length3
Mean length4.11065
Min length2

Characters and Unicode

Total characters164426
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowyes
3rd rowno
4th rowyes
5th rowunknown

Common Values

ValueCountFrequency (%)
unknown 14477
36.2%
no 13482
33.7%
yes 12041
30.1%

Length

2025-07-25T15:20:33.922995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:33.969029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
unknown 14477
36.2%
no 13482
33.7%
yes 12041
30.1%

Most occurring characters

ValueCountFrequency (%)
n 56913
34.6%
o 27959
17.0%
u 14477
 
8.8%
k 14477
 
8.8%
w 14477
 
8.8%
y 12041
 
7.3%
e 12041
 
7.3%
s 12041
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 164426
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 56913
34.6%
o 27959
17.0%
u 14477
 
8.8%
k 14477
 
8.8%
w 14477
 
8.8%
y 12041
 
7.3%
e 12041
 
7.3%
s 12041
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 164426
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 56913
34.6%
o 27959
17.0%
u 14477
 
8.8%
k 14477
 
8.8%
w 14477
 
8.8%
y 12041
 
7.3%
e 12041
 
7.3%
s 12041
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 164426
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 56913
34.6%
o 27959
17.0%
u 14477
 
8.8%
k 14477
 
8.8%
w 14477
 
8.8%
y 12041
 
7.3%
e 12041
 
7.3%
s 12041
 
7.3%

Bodily_Injuries
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
1.22203168311886
13381 
-1.226254977608615
13312 
-0.0021116472448774
13307 

Length

Max length19
Median length18
Mean length17.663625
Min length16

Characters and Unicode

Total characters706545
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1.226254977608615
2nd row1.22203168311886
3rd row-1.226254977608615
4th row1.22203168311886
5th row-1.226254977608615

Common Values

ValueCountFrequency (%)
1.22203168311886 13381
33.5%
-1.226254977608615 13312
33.3%
-0.0021116472448774 13307
33.3%

Length

2025-07-25T15:20:34.030889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:34.079473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.22203168311886 13381
33.5%
1.226254977608615 13312
33.3%
0.0021116472448774 13307
33.3%

Most occurring characters

ValueCountFrequency (%)
1 120069
17.0%
2 106693
15.1%
6 80005
11.3%
8 66762
9.4%
0 66614
9.4%
7 66545
9.4%
4 66540
9.4%
. 40000
 
5.7%
3 26762
 
3.8%
5 26624
 
3.8%
Other values (2) 39931
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 706545
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 120069
17.0%
2 106693
15.1%
6 80005
11.3%
8 66762
9.4%
0 66614
9.4%
7 66545
9.4%
4 66540
9.4%
. 40000
 
5.7%
3 26762
 
3.8%
5 26624
 
3.8%
Other values (2) 39931
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 706545
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 120069
17.0%
2 106693
15.1%
6 80005
11.3%
8 66762
9.4%
0 66614
9.4%
7 66545
9.4%
4 66540
9.4%
. 40000
 
5.7%
3 26762
 
3.8%
5 26624
 
3.8%
Other values (2) 39931
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 706545
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 120069
17.0%
2 106693
15.1%
6 80005
11.3%
8 66762
9.4%
0 66614
9.4%
7 66545
9.4%
4 66540
9.4%
. 40000
 
5.7%
3 26762
 
3.8%
5 26624
 
3.8%
Other values (2) 39931
 
5.7%

Witnesses
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
-0.4329664155110092
10231 
-1.3299807947745077
10108 
0.4640479637524894
9907 
1.361062343015988
9754 

Length

Max length19
Median length19
Mean length18.264625
Min length17

Characters and Unicode

Total characters730585
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.4640479637524894
2nd row-1.3299807947745077
3rd row0.4640479637524894
4th row1.361062343015988
5th row-0.4329664155110092

Common Values

ValueCountFrequency (%)
-0.4329664155110092 10231
25.6%
-1.3299807947745077 10108
25.3%
0.4640479637524894 9907
24.8%
1.361062343015988 9754
24.4%

Length

2025-07-25T15:20:34.147259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:34.200277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.4329664155110092 10231
25.6%
1.3299807947745077 10108
25.3%
0.4640479637524894 9907
24.8%
1.361062343015988 9754
24.4%

Most occurring characters

ValueCountFrequency (%)
4 99967
13.7%
0 90231
12.4%
9 80354
11.0%
7 70354
9.6%
1 70063
9.6%
6 59784
8.2%
3 59508
8.1%
2 50231
6.9%
5 50231
6.9%
. 40000
5.5%
Other values (2) 59862
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 730585
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 99967
13.7%
0 90231
12.4%
9 80354
11.0%
7 70354
9.6%
1 70063
9.6%
6 59784
8.2%
3 59508
8.1%
2 50231
6.9%
5 50231
6.9%
. 40000
5.5%
Other values (2) 59862
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 730585
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 99967
13.7%
0 90231
12.4%
9 80354
11.0%
7 70354
9.6%
1 70063
9.6%
6 59784
8.2%
3 59508
8.1%
2 50231
6.9%
5 50231
6.9%
. 40000
5.5%
Other values (2) 59862
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 730585
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 99967
13.7%
0 90231
12.4%
9 80354
11.0%
7 70354
9.6%
1 70063
9.6%
6 59784
8.2%
3 59508
8.1%
2 50231
6.9%
5 50231
6.9%
. 40000
5.5%
Other values (2) 59862
8.2%

Police_Report
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
not available
13716 
no
13634 
yes
12650 

Length

Max length13
Median length3
Mean length6.08815
Min length2

Characters and Unicode

Total characters243526
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rownot available
3rd rowno
4th rownot available
5th rownot available

Common Values

ValueCountFrequency (%)
not available 13716
34.3%
no 13634
34.1%
yes 12650
31.6%

Length

2025-07-25T15:20:34.271739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:34.316255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
not 13716
25.5%
available 13716
25.5%
no 13634
25.4%
yes 12650
23.5%

Most occurring characters

ValueCountFrequency (%)
a 41148
16.9%
l 27432
11.3%
n 27350
11.2%
o 27350
11.2%
e 26366
10.8%
t 13716
 
5.6%
13716
 
5.6%
v 13716
 
5.6%
i 13716
 
5.6%
b 13716
 
5.6%
Other values (2) 25300
10.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 243526
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 41148
16.9%
l 27432
11.3%
n 27350
11.2%
o 27350
11.2%
e 26366
10.8%
t 13716
 
5.6%
13716
 
5.6%
v 13716
 
5.6%
i 13716
 
5.6%
b 13716
 
5.6%
Other values (2) 25300
10.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 243526
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 41148
16.9%
l 27432
11.3%
n 27350
11.2%
o 27350
11.2%
e 26366
10.8%
t 13716
 
5.6%
13716
 
5.6%
v 13716
 
5.6%
i 13716
 
5.6%
b 13716
 
5.6%
Other values (2) 25300
10.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 243526
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 41148
16.9%
l 27432
11.3%
n 27350
11.2%
o 27350
11.2%
e 26366
10.8%
t 13716
 
5.6%
13716
 
5.6%
v 13716
 
5.6%
i 13716
 
5.6%
b 13716
 
5.6%
Other values (2) 25300
10.4%
Distinct365
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
Minimum2024-01-02 00:00:00
Maximum2026-05-09 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-25T15:20:34.385059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:34.476729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct66
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
Minimum2024-01-01 00:00:00
Maximum2024-03-06 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-25T15:20:34.565244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:34.653571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Auto_Make
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4779289 × 10-16
Minimum-1.6333896
Maximum1.6044484
Zeros0
Zeros (%)0.0%
Negative19645
Negative (%)49.1%
Memory size312.6 KiB
2025-07-25T15:20:34.812596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.6333896
5-th percentile-1.6333896
Q1-0.88619626
median0.11006159
Q30.85725497
95-th percentile1.6044484
Maximum1.6044484
Range3.237838
Interquartile range (IQR)1.7434512

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)6.76631 × 1015
Kurtosis-1.2371413
Mean1.4779289 × 10-16
Median Absolute Deviation (MAD)0.74719339
Skewness-0.023177176
Sum4.0927262 × 10-12
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:34.868959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
-0.6371318007 3255
 
8.1%
1.106319434 3247
 
8.1%
0.8572549723 3118
 
7.8%
0.6081905102 3108
 
7.8%
-0.8861962628 3085
 
7.7%
-0.3880673385 2889
 
7.2%
1.355383897 2782
 
7.0%
0.359126048 2742
 
6.9%
-1.135260725 2736
 
6.8%
-1.633389649 2713
 
6.8%
Other values (4) 10325
25.8%
ValueCountFrequency (%)
-1.633389649 2713
6.8%
-1.384325187 2701
6.8%
-1.135260725 2736
6.8%
-0.8861962628 3085
7.7%
-0.6371318007 3255
8.1%
-0.3880673385 2889
7.2%
-0.1390028763 2266
5.7%
0.1100615858 2668
6.7%
0.359126048 2742
6.9%
0.6081905102 3108
7.8%
ValueCountFrequency (%)
1.604448359 2690
6.7%
1.355383897 2782
7.0%
1.106319434 3247
8.1%
0.8572549723 3118
7.8%
0.6081905102 3108
7.8%
0.359126048 2742
6.9%
0.1100615858 2668
6.7%
-0.1390028763 2266
5.7%
-0.3880673385 2889
7.2%
-0.6371318007 3255
8.1%

Auto_Model
Real number (ℝ)

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9737992 × 10-17
Minimum-1.7422902
Maximum1.6811857
Zeros0
Zeros (%)0.0%
Negative19344
Negative (%)48.4%
Memory size312.6 KiB
2025-07-25T15:20:34.939396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.7422902
5-th percentile-1.5621073
Q1-0.84137552
median0.059539202
Q30.87036245
95-th percentile1.5010028
Maximum1.6811857
Range3.4234759
Interquartile range (IQR)1.711738

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)2.0105607 × 1016
Kurtosis-1.1913337
Mean4.9737992 × 10-17
Median Absolute Deviation (MAD)0.81082325
Skewness-0.076487036
Sum2.8990144 × 10-12
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:35.016509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.9604539253 1751
 
4.4%
1.501002759 1663
 
4.2%
0.6901795083 1504
 
3.8%
-1.381924356 1489
 
3.7%
0.3298136189 1443
 
3.6%
0.05953920179 1399
 
3.5%
0.7802709806 1291
 
3.2%
0.1496306741 1286
 
3.2%
-1.291832884 1212
 
3.0%
0.870362453 1188
 
3.0%
Other values (29) 25774
64.4%
ValueCountFrequency (%)
-1.742290245 697
1.7%
-1.652198773 1047
2.6%
-1.562107301 1007
2.5%
-1.472015828 1064
2.7%
-1.381924356 1489
3.7%
-1.291832884 1212
3.0%
-1.201741411 541
 
1.4%
-1.111649939 767
1.9%
-1.021558466 836
2.1%
-0.9314669941 1088
2.7%
ValueCountFrequency (%)
1.681185704 625
 
1.6%
1.591094232 885
2.2%
1.501002759 1663
4.2%
1.410911287 954
2.4%
1.320819815 987
2.5%
1.230728342 828
2.1%
1.14063687 947
2.4%
1.050545398 442
 
1.1%
0.9604539253 1751
4.4%
0.870362453 1188
3.0%

Auto_Year
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7534952 × 10-14
Minimum-1.5838775
Maximum1.5741228
Zeros0
Zeros (%)0.0%
Negative19395
Negative (%)48.5%
Memory size312.6 KiB
2025-07-25T15:20:35.078816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.5838775
5-th percentile-1.5838775
Q1-0.88209967
median0.17056711
Q30.87234496
95-th percentile1.5741228
Maximum1.5741228
Range3.1580003
Interquartile range (IQR)1.7544446

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)3.6317931 × 1013
Kurtosis-1.2317176
Mean2.7534952 × 10-14
Median Absolute Deviation (MAD)0.70177785
Skewness-0.079136501
Sum1.1013981 × 10-9
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:35.135037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.87234496 5792
14.5%
-1.583877525 4326
10.8%
0.5214560335 4019
10.0%
-0.5312107457 3970
9.9%
0.1705671071 3816
9.5%
-0.8820996721 3816
9.5%
-1.232988599 3679
9.2%
1.223233886 3621
9.1%
-0.1803218193 3604
9.0%
1.574122813 3357
8.4%
ValueCountFrequency (%)
-1.583877525 4326
10.8%
-1.232988599 3679
9.2%
-0.8820996721 3816
9.5%
-0.5312107457 3970
9.9%
-0.1803218193 3604
9.0%
0.1705671071 3816
9.5%
0.5214560335 4019
10.0%
0.87234496 5792
14.5%
1.223233886 3621
9.1%
1.574122813 3357
8.4%
ValueCountFrequency (%)
1.574122813 3357
8.4%
1.223233886 3621
9.1%
0.87234496 5792
14.5%
0.5214560335 4019
10.0%
0.1705671071 3816
9.5%
-0.1803218193 3604
9.0%
-0.5312107457 3970
9.9%
-0.8820996721 3816
9.5%
-1.232988599 3679
9.2%
-1.583877525 4326
10.8%

Vehicle_Color
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
white
10261 
black
9173 
gray
7357 
blue
5158 
silver
4425 

Length

Max length6
Median length5
Mean length4.61645
Min length3

Characters and Unicode

Total characters184658
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowblack
3rd rowgray
4th rowgray
5th rowgray

Common Values

ValueCountFrequency (%)
white 10261
25.7%
black 9173
22.9%
gray 7357
18.4%
blue 5158
12.9%
silver 4425
11.1%
red 3626
 
9.1%

Length

2025-07-25T15:20:35.203083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:35.262654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
white 10261
25.7%
black 9173
22.9%
gray 7357
18.4%
blue 5158
12.9%
silver 4425
11.1%
red 3626
 
9.1%

Most occurring characters

ValueCountFrequency (%)
e 23470
12.7%
l 18756
10.2%
a 16530
 
9.0%
r 15408
 
8.3%
i 14686
 
8.0%
b 14331
 
7.8%
w 10261
 
5.6%
t 10261
 
5.6%
h 10261
 
5.6%
c 9173
 
5.0%
Other values (7) 41521
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 184658
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 23470
12.7%
l 18756
10.2%
a 16530
 
9.0%
r 15408
 
8.3%
i 14686
 
8.0%
b 14331
 
7.8%
w 10261
 
5.6%
t 10261
 
5.6%
h 10261
 
5.6%
c 9173
 
5.0%
Other values (7) 41521
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 184658
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 23470
12.7%
l 18756
10.2%
a 16530
 
9.0%
r 15408
 
8.3%
i 14686
 
8.0%
b 14331
 
7.8%
w 10261
 
5.6%
t 10261
 
5.6%
h 10261
 
5.6%
c 9173
 
5.0%
Other values (7) 41521
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 184658
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 23470
12.7%
l 18756
10.2%
a 16530
 
9.0%
r 15408
 
8.3%
i 14686
 
8.0%
b 14331
 
7.8%
w 10261
 
5.6%
t 10261
 
5.6%
h 10261
 
5.6%
c 9173
 
5.0%
Other values (7) 41521
22.5%

Vehicle_Cost
Real number (ℝ)

High correlation 

Distinct39468
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0411229 × 10-16
Minimum-1.3650188
Maximum5.6503158
Zeros0
Zeros (%)0.0%
Negative22052
Negative (%)55.1%
Memory size312.6 KiB
2025-07-25T15:20:35.346281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.3650188
5-th percentile-1.1758754
Q1-0.83133194
median-0.16961064
Q30.5601403
95-th percentile1.5641268
Maximum5.6503158
Range7.0153346
Interquartile range (IQR)1.3914722

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)3.2883002 × 1015
Kurtosis2.6221301
Mean3.0411229 × 10-16
Median Absolute Deviation (MAD)0.68712186
Skewness1.263502
Sum1.2320811 × 10-11
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:35.434801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8251590071 3
 
< 0.1%
-0.4568497829 3
 
< 0.1%
-0.877973772 3
 
< 0.1%
-1.1851191 3
 
< 0.1%
0.5948391958 3
 
< 0.1%
-0.1129762429 3
 
< 0.1%
-0.4699972821 3
 
< 0.1%
-1.192550666 3
 
< 0.1%
-1.162922559 3
 
< 0.1%
-0.1772527478 3
 
< 0.1%
Other values (39458) 39970
99.9%
ValueCountFrequency (%)
-1.36501877 1
< 0.1%
-1.364781199 1
< 0.1%
-1.364314593 1
< 0.1%
-1.364166644 1
< 0.1%
-1.363499453 1
< 0.1%
-1.362216285 1
< 0.1%
-1.361258887 1
< 0.1%
-1.360802239 1
< 0.1%
-1.358964263 1
< 0.1%
-1.35882485 1
< 0.1%
ValueCountFrequency (%)
5.650315835 1
< 0.1%
5.629793673 1
< 0.1%
5.61793361 1
< 0.1%
5.617090019 1
< 0.1%
5.599697536 1
< 0.1%
5.563046144 1
< 0.1%
5.557099752 1
< 0.1%
5.546101774 1
< 0.1%
5.520964757 1
< 0.1%
5.513627081 1
< 0.1%

Annual_Mileage
Real number (ℝ)

High correlation 

Distinct12420
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0818902 × 10-16
Minimum-1.7366244
Maximum1.728694
Zeros0
Zeros (%)0.0%
Negative19972
Negative (%)49.9%
Memory size312.6 KiB
2025-07-25T15:20:35.517571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.7366244
5-th percentile-1.5630919
Q1-0.86523008
median0.0026989033
Q30.8608317
95-th percentile1.5636916
Maximum1.728694
Range3.4653184
Interquartile range (IQR)1.7260618

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)4.8033873 × 1015
Kurtosis-1.1939645
Mean2.0818902 × 10-16
Median Absolute Deviation (MAD)0.86286429
Skewness-0.0036930663
Sum8.2716056 × 10-12
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:35.600372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.076081383 12
 
< 0.1%
-0.8972176389 11
 
< 0.1%
0.6765700581 11
 
< 0.1%
-1.557760646 11
 
< 0.1%
-0.8396400402 11
 
< 0.1%
0.9444658297 11
 
< 0.1%
-1.667851147 10
 
< 0.1%
-0.5605486244 10
 
< 0.1%
-1.189104077 10
 
< 0.1%
0.2114176985 10
 
< 0.1%
Other values (12410) 39893
99.7%
ValueCountFrequency (%)
-1.73662439 2
 
< 0.1%
-1.736357827 5
< 0.1%
-1.735824701 2
 
< 0.1%
-1.735558138 2
 
< 0.1%
-1.735291575 3
< 0.1%
-1.735025012 4
< 0.1%
-1.734758449 4
< 0.1%
-1.734491886 3
< 0.1%
-1.734225323 6
< 0.1%
-1.73395876 2
 
< 0.1%
ValueCountFrequency (%)
1.728694049 3
 
< 0.1%
1.728427486 2
 
< 0.1%
1.728160923 2
 
< 0.1%
1.72789436 4
< 0.1%
1.727627797 2
 
< 0.1%
1.727361234 1
 
< 0.1%
1.727094671 6
< 0.1%
1.726828108 9
< 0.1%
1.726561545 2
 
< 0.1%
1.726294982 2
 
< 0.1%

DiffIN_Mileage
Real number (ℝ)

Distinct5991
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9528385 × 10-17
Minimum-1.7258103
Maximum1.7355824
Zeros0
Zeros (%)0.0%
Negative19953
Negative (%)49.9%
Memory size312.6 KiB
2025-07-25T15:20:35.683496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.7258103
5-th percentile-1.5527407
Q1-0.87430769
median0.0037322466
Q30.85826356
95-th percentile1.5584745
Maximum1.7355824
Range3.4613927
Interquartile range (IQR)1.7325713

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)1.1169782 × 1016
Kurtosis-1.2042321
Mean8.9528385 × 10-17
Median Absolute Deviation (MAD)0.86650196
Skewness0.0015414506
Sum3.688494 × 10-12
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:35.767141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.522165021 16
 
< 0.1%
1.146568725 16
 
< 0.1%
-1.184679236 16
 
< 0.1%
0.6238984328 16
 
< 0.1%
0.01988541242 16
 
< 0.1%
0.685626602 16
 
< 0.1%
-1.211793478 16
 
< 0.1%
-0.008382627691 16
 
< 0.1%
0.4456367104 16
 
< 0.1%
0.3798702498 15
 
< 0.1%
Other values (5981) 39841
99.6%
ValueCountFrequency (%)
-1.725810289 9
< 0.1%
-1.72523339 8
< 0.1%
-1.724656492 3
 
< 0.1%
-1.724079593 4
 
< 0.1%
-1.723502694 10
< 0.1%
-1.722925795 3
 
< 0.1%
-1.722348897 7
< 0.1%
-1.721771998 10
< 0.1%
-1.721195099 5
< 0.1%
-1.7206182 7
< 0.1%
ValueCountFrequency (%)
1.735582378 8
< 0.1%
1.735005479 8
< 0.1%
1.73442858 9
< 0.1%
1.733851681 6
< 0.1%
1.733274783 2
 
< 0.1%
1.732697884 9
< 0.1%
1.732120985 4
< 0.1%
1.731544086 6
< 0.1%
1.730967187 6
< 0.1%
1.730390289 5
< 0.1%

Low_Mileage_Discount
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
-0.4852659826135434
32376 
2.060725531623305
7624 

Length

Max length19
Median length19
Mean length18.6188
Min length17

Characters and Unicode

Total characters744752
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.4852659826135434
2nd row2.060725531623305
3rd row-0.4852659826135434
4th row-0.4852659826135434
5th row2.060725531623305

Common Values

ValueCountFrequency (%)
-0.4852659826135434 32376
80.9%
2.060725531623305 7624
 
19.1%

Length

2025-07-25T15:20:35.851230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:35.901601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.4852659826135434 32376
80.9%
2.060725531623305 7624
 
19.1%

Most occurring characters

ValueCountFrequency (%)
5 120000
16.1%
4 97128
13.0%
2 87624
11.8%
3 87624
11.8%
6 80000
10.7%
8 64752
8.7%
0 55248
7.4%
. 40000
 
5.4%
1 40000
 
5.4%
- 32376
 
4.3%
Other values (2) 40000
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 744752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 120000
16.1%
4 97128
13.0%
2 87624
11.8%
3 87624
11.8%
6 80000
10.7%
8 64752
8.7%
0 55248
7.4%
. 40000
 
5.4%
1 40000
 
5.4%
- 32376
 
4.3%
Other values (2) 40000
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 744752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 120000
16.1%
4 97128
13.0%
2 87624
11.8%
3 87624
11.8%
6 80000
10.7%
8 64752
8.7%
0 55248
7.4%
. 40000
 
5.4%
1 40000
 
5.4%
- 32376
 
4.3%
Other values (2) 40000
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 744752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 120000
16.1%
4 97128
13.0%
2 87624
11.8%
3 87624
11.8%
6 80000
10.7%
8 64752
8.7%
0 55248
7.4%
. 40000
 
5.4%
1 40000
 
5.4%
- 32376
 
4.3%
Other values (2) 40000
 
5.4%

Commute_Discount
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
-0.1413004994494239
39217 
7.07711581980595
 
783

Length

Max length19
Median length19
Mean length18.941275
Min length16

Characters and Unicode

Total characters757651
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.1413004994494239
2nd row-0.1413004994494239
3rd row-0.1413004994494239
4th row-0.1413004994494239
5th row-0.1413004994494239

Common Values

ValueCountFrequency (%)
-0.1413004994494239 39217
98.0%
7.07711581980595 783
 
2.0%

Length

2025-07-25T15:20:35.956875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:36.003038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.1413004994494239 39217
98.0%
7.07711581980595 783
 
2.0%

Most occurring characters

ValueCountFrequency (%)
4 196085
25.9%
9 158434
20.9%
0 119217
15.7%
1 80783
10.7%
3 78434
 
10.4%
. 40000
 
5.3%
- 39217
 
5.2%
2 39217
 
5.2%
7 2349
 
0.3%
5 2349
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 757651
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 196085
25.9%
9 158434
20.9%
0 119217
15.7%
1 80783
10.7%
3 78434
 
10.4%
. 40000
 
5.3%
- 39217
 
5.2%
2 39217
 
5.2%
7 2349
 
0.3%
5 2349
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 757651
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 196085
25.9%
9 158434
20.9%
0 119217
15.7%
1 80783
10.7%
3 78434
 
10.4%
. 40000
 
5.3%
- 39217
 
5.2%
2 39217
 
5.2%
7 2349
 
0.3%
5 2349
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 757651
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 196085
25.9%
9 158434
20.9%
0 119217
15.7%
1 80783
10.7%
3 78434
 
10.4%
. 40000
 
5.3%
- 39217
 
5.2%
2 39217
 
5.2%
7 2349
 
0.3%
5 2349
 
0.3%

Total_Claim
Real number (ℝ)

High correlation 

Distinct39692
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2434498 × 10-17
Minimum-1.1479923
Maximum9.4606867
Zeros0
Zeros (%)0.0%
Negative21650
Negative (%)54.1%
Memory size312.6 KiB
2025-07-25T15:20:36.063644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.1479923
5-th percentile-1.0413231
Q1-0.61560882
median-0.082994881
Q30.454122
95-th percentile0.87860256
Maximum9.4606867
Range10.608679
Interquartile range (IQR)1.0697308

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)-8.0422427 × 1016
Kurtosis30.522216
Mean-1.2434498 × 10-17
Median Absolute Deviation (MAD)0.53474081
Skewness4.329865
Sum-4.2632564 × 10-13
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:36.146160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.05454437 2
 
< 0.1%
-0.6093729012 2
 
< 0.1%
-0.09138625898 2
 
< 0.1%
0.6301664007 2
 
< 0.1%
-0.7270245925 2
 
< 0.1%
-0.7695626988 2
 
< 0.1%
0.06815660373 2
 
< 0.1%
-0.0008512127823 2
 
< 0.1%
0.4482701434 2
 
< 0.1%
0.8240326615 2
 
< 0.1%
Other values (39682) 39980
> 99.9%
ValueCountFrequency (%)
-1.14799233 1
< 0.1%
-1.147968433 1
< 0.1%
-1.14793344 1
< 0.1%
-1.147897594 1
< 0.1%
-1.147743115 1
< 0.1%
-1.147725192 1
< 0.1%
-1.147645819 1
< 0.1%
-1.147624482 1
< 0.1%
-1.147569006 1
< 0.1%
-1.147565592 1
< 0.1%
ValueCountFrequency (%)
9.460686713 1
< 0.1%
9.460067943 1
< 0.1%
9.448440183 1
< 0.1%
9.438832602 1
< 0.1%
9.421078591 1
< 0.1%
9.42030022 1
< 0.1%
9.419995529 1
< 0.1%
9.405226124 1
< 0.1%
9.381313428 1
< 0.1%
9.375240945 1
< 0.1%

Injury_Claim
Real number (ℝ)

High correlation 

Distinct39044
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.1368684 × 10-17
Minimum-0.81245373
Maximum18.668374
Zeros0
Zeros (%)0.0%
Negative25753
Negative (%)64.4%
Memory size312.6 KiB
2025-07-25T15:20:36.363930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.81245373
5-th percentile-0.78331371
Q1-0.62456889
median-0.29671981
Q30.31768876
95-th percentile1.6176262
Maximum18.668374
Range19.480828
Interquartile range (IQR)0.94225765

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)-8.796203 × 1016
Kurtosis44.933067
Mean-1.1368684 × 10-17
Median Absolute Deviation (MAD)0.39345474
Skewness4.7503209
Sum-5.7553962 × 10-13
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:36.469562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7207728952 3
 
< 0.1%
-0.6482388638 3
 
< 0.1%
-0.7057899663 3
 
< 0.1%
-0.6976238484 3
 
< 0.1%
-0.6897669467 3
 
< 0.1%
-0.6870173824 3
 
< 0.1%
-0.6786667889 3
 
< 0.1%
0.05769813652 3
 
< 0.1%
-0.6479735703 3
 
< 0.1%
-0.4615917675 3
 
< 0.1%
Other values (39034) 39970
99.9%
ValueCountFrequency (%)
-0.8124537325 1
< 0.1%
-0.812443191 1
< 0.1%
-0.8124326496 1
< 0.1%
-0.8124221081 1
< 0.1%
-0.8124185943 1
< 0.1%
-0.8124150805 1
< 0.1%
-0.8124062959 1
< 0.1%
-0.8123975114 1
< 0.1%
-0.8123904837 1
< 0.1%
-0.8123799423 1
< 0.1%
ValueCountFrequency (%)
18.66837399 1
< 0.1%
17.53461534 1
< 0.1%
16.57753859 1
< 0.1%
15.22987438 1
< 0.1%
14.80712141 1
< 0.1%
14.78669382 1
< 0.1%
14.64874827 1
< 0.1%
14.41600864 1
< 0.1%
13.75577238 1
< 0.1%
13.70429492 1
< 0.1%

Property_Claim
Real number (ℝ)

High correlation 

Distinct39024
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.2580162 × 10-17
Minimum-0.80510921
Maximum18.025179
Zeros0
Zeros (%)0.0%
Negative25773
Negative (%)64.4%
Memory size312.6 KiB
2025-07-25T15:20:36.555403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.80510921
5-th percentile-0.77696212
Q1-0.62248387
median-0.29690765
Q30.313623
95-th percentile1.6053163
Maximum18.025179
Range18.830288
Interquartile range (IQR)0.93610687

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)-1.9018817 × 1016
Kurtosis45.953359
Mean-5.2580162 × 10-17
Median Absolute Deviation (MAD)0.39057694
Skewness4.8200446
Sum-2.4265034 × 10-12
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:36.645763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7321522108 3
 
< 0.1%
-0.7895726468 3
 
< 0.1%
-0.7475231986 3
 
< 0.1%
-0.631838923 3
 
< 0.1%
-0.7607796408 3
 
< 0.1%
-0.7662902227 3
 
< 0.1%
-0.7033195355 3
 
< 0.1%
-0.8030377839 3
 
< 0.1%
-0.7212190094 3
 
< 0.1%
-0.6891179294 3
 
< 0.1%
Other values (39014) 39970
99.9%
ValueCountFrequency (%)
-0.8051092108 1
< 0.1%
-0.8050902385 2
< 0.1%
-0.8050816148 1
< 0.1%
-0.805059193 1
< 0.1%
-0.8050471197 1
< 0.1%
-0.805045395 1
< 0.1%
-0.8050436702 1
< 0.1%
-0.8050350465 1
< 0.1%
-0.8050264227 1
< 0.1%
-0.8050160742 1
< 0.1%
ValueCountFrequency (%)
18.02517872 1
< 0.1%
16.96661404 1
< 0.1%
15.6439709 1
< 0.1%
15.50131323 1
< 0.1%
15.19992148 1
< 0.1%
15.16242538 1
< 0.1%
14.9583631 1
< 0.1%
14.92810405 1
< 0.1%
14.72038357 1
< 0.1%
14.68953639 1
< 0.1%

Vehicle_Claim
Real number (ℝ)

High correlation 

Distinct39027
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.4502184 × 10-17
Minimum-0.7823085
Maximum19.946866
Zeros0
Zeros (%)0.0%
Negative25790
Negative (%)64.5%
Memory size312.6 KiB
2025-07-25T15:20:36.734282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.7823085
5-th percentile-0.75374817
Q1-0.60280374
median-0.29010848
Q30.29815241
95-th percentile1.5523339
Maximum19.946866
Range20.729174
Interquartile range (IQR)0.90095615

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)-1.0581898 × 1016
Kurtosis59.597341
Mean-9.4502184 × 10-17
Median Absolute Deviation (MAD)0.37618309
Skewness5.5330453
Sum-3.907985 × 10-12
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:36.820520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7667587463 4
 
< 0.1%
-0.7729836847 3
 
< 0.1%
-0.6855809813 3
 
< 0.1%
-0.654955023 3
 
< 0.1%
-0.7654036005 3
 
< 0.1%
-0.4162965585 3
 
< 0.1%
-0.7226552168 3
 
< 0.1%
-0.6309284734 3
 
< 0.1%
-0.7413804094 3
 
< 0.1%
-0.6455680777 3
 
< 0.1%
Other values (39017) 39969
99.9%
ValueCountFrequency (%)
-0.7823084981 1
< 0.1%
-0.7822967434 1
< 0.1%
-0.7822681964 1
< 0.1%
-0.7822648379 1
< 0.1%
-0.7822598002 1
< 0.1%
-0.7822581209 1
< 0.1%
-0.7822530832 1
< 0.1%
-0.7822379701 1
< 0.1%
-0.7821825552 1
< 0.1%
-0.782167442 1
< 0.1%
ValueCountFrequency (%)
19.94686585 1
< 0.1%
19.41019623 1
< 0.1%
17.82257832 1
< 0.1%
16.7016528 1
< 0.1%
16.37306605 1
< 0.1%
15.93712893 1
< 0.1%
15.63261239 1
< 0.1%
15.53011165 1
< 0.1%
15.28927858 1
< 0.1%
15.20890013 1
< 0.1%

Vehicle_Registration
Real number (ℝ)

Uniform  Unique 

Distinct40000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.6645353 × 10-18
Minimum-1.7320075
Maximum1.7320075
Zeros0
Zeros (%)0.0%
Negative20000
Negative (%)50.0%
Memory size312.6 KiB
2025-07-25T15:20:36.905291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.7320075
5-th percentile-1.5588068
Q1-0.86600375
median0
Q30.86600375
95-th percentile1.5588068
Maximum1.7320075
Range3.464015
Interquartile range (IQR)1.7320075

Descriptive statistics

Standard deviation1.0000125
Coefficient of variation (CV)-3.7530466 × 1017
Kurtosis-1.2
Mean-2.6645353 × 10-18
Median Absolute Deviation (MAD)0.8660254
Skewness1.0658541 × 10-18
Sum7.4606987 × 10-14
Variance1.000025
MonotonicityNot monotonic
2025-07-25T15:20:36.992349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.132371517 1
 
< 0.1%
-0.5526541116 1
 
< 0.1%
1.515847566 1
 
< 0.1%
1.50883276 1
 
< 0.1%
0.1496924911 1
 
< 0.1%
0.02836233198 1
 
< 0.1%
1.054169423 1
 
< 0.1%
-1.449423417 1
 
< 0.1%
-0.4758376583 1
 
< 0.1%
1.22451662 1
 
< 0.1%
Other values (39990) 39990
> 99.9%
ValueCountFrequency (%)
-1.732007507 1
< 0.1%
-1.731920904 1
< 0.1%
-1.731834302 1
< 0.1%
-1.731747699 1
< 0.1%
-1.731661097 1
< 0.1%
-1.731574494 1
< 0.1%
-1.731487892 1
< 0.1%
-1.731401289 1
< 0.1%
-1.731314687 1
< 0.1%
-1.731228084 1
< 0.1%
ValueCountFrequency (%)
1.732007507 1
< 0.1%
1.731920904 1
< 0.1%
1.731834302 1
< 0.1%
1.731747699 1
< 0.1%
1.731661097 1
< 0.1%
1.731574494 1
< 0.1%
1.731487892 1
< 0.1%
1.731401289 1
< 0.1%
1.731314687 1
< 0.1%
1.731228084 1
< 0.1%

Check_Point
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
0.0
40000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters120000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 40000
100.0%

Length

2025-07-25T15:20:37.070538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:37.110104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 40000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 80000
66.7%
. 40000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 120000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 80000
66.7%
. 40000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 120000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 80000
66.7%
. 40000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 120000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 80000
66.7%
. 40000
33.3%

Policy_BI_Low
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
-0.1398605188006537
14047 
-1.0687034130437465
13962 
1.4082109716045008
11991 

Length

Max length19
Median length19
Mean length18.700225
Min length18

Characters and Unicode

Total characters748009
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.4082109716045008
2nd row-0.1398605188006537
3rd row1.4082109716045008
4th row1.4082109716045008
5th row-0.1398605188006537

Common Values

ValueCountFrequency (%)
-0.1398605188006537 14047
35.1%
-1.0687034130437465 13962
34.9%
1.4082109716045008 11991
30.0%

Length

2025-07-25T15:20:37.159704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:37.212849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.1398605188006537 14047
35.1%
1.0687034130437465 13962
34.9%
1.4082109716045008 11991
30.0%

Most occurring characters

ValueCountFrequency (%)
0 158029
21.1%
1 91991
12.3%
8 80085
10.7%
3 69980
9.4%
6 68009
9.1%
4 65868
8.8%
5 54047
 
7.2%
7 53962
 
7.2%
. 40000
 
5.3%
- 28009
 
3.7%
Other values (2) 38029
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 748009
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 158029
21.1%
1 91991
12.3%
8 80085
10.7%
3 69980
9.4%
6 68009
9.1%
4 65868
8.8%
5 54047
 
7.2%
7 53962
 
7.2%
. 40000
 
5.3%
- 28009
 
3.7%
Other values (2) 38029
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 748009
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 158029
21.1%
1 91991
12.3%
8 80085
10.7%
3 69980
9.4%
6 68009
9.1%
4 65868
8.8%
5 54047
 
7.2%
7 53962
 
7.2%
. 40000
 
5.3%
- 28009
 
3.7%
Other values (2) 38029
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 748009
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 158029
21.1%
1 91991
12.3%
8 80085
10.7%
3 69980
9.4%
6 68009
9.1%
4 65868
8.8%
5 54047
 
7.2%
7 53962
 
7.2%
. 40000
 
5.3%
- 28009
 
3.7%
Other values (2) 38029
 
5.1%

Policy_BI_High
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size312.6 KiB
-0.2788053169707772
14047 
-0.9751440312682448
13962 
1.4620414687728915
11991 

Length

Max length19
Median length19
Mean length18.700225
Min length18

Characters and Unicode

Total characters748009
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.4620414687728915
2nd row-0.2788053169707772
3rd row1.4620414687728915
4th row1.4620414687728915
5th row-0.2788053169707772

Common Values

ValueCountFrequency (%)
-0.2788053169707772 14047
35.1%
-0.9751440312682448 13962
34.9%
1.4620414687728915 11991
30.0%

Length

2025-07-25T15:20:37.279328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-25T15:20:37.330850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.2788053169707772 14047
35.1%
0.9751440312682448 13962
34.9%
1.4620414687728915 11991
30.0%

Most occurring characters

ValueCountFrequency (%)
7 108179
14.5%
4 91821
12.3%
0 82056
11.0%
2 80000
10.7%
8 80000
10.7%
1 77944
10.4%
6 51991
7.0%
. 40000
 
5.3%
9 40000
 
5.3%
5 40000
 
5.3%
Other values (2) 56018
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 748009
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 108179
14.5%
4 91821
12.3%
0 82056
11.0%
2 80000
10.7%
8 80000
10.7%
1 77944
10.4%
6 51991
7.0%
. 40000
 
5.3%
9 40000
 
5.3%
5 40000
 
5.3%
Other values (2) 56018
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 748009
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 108179
14.5%
4 91821
12.3%
0 82056
11.0%
2 80000
10.7%
8 80000
10.7%
1 77944
10.4%
6 51991
7.0%
. 40000
 
5.3%
9 40000
 
5.3%
5 40000
 
5.3%
Other values (2) 56018
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 748009
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 108179
14.5%
4 91821
12.3%
0 82056
11.0%
2 80000
10.7%
8 80000
10.7%
1 77944
10.4%
6 51991
7.0%
. 40000
 
5.3%
9 40000
 
5.3%
5 40000
 
5.3%
Other values (2) 56018
7.5%

Interactions

2025-07-25T15:20:26.658897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:49.992037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:51.655464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:53.211418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:54.855435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:56.494345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:57.959498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-25T15:20:15.949370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:17.608007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:19.351913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:21.021710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:22.737892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:24.604209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:26.262494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:27.957284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:51.310222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:52.894525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:54.519779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:56.069403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:57.643389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:59.238317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:00.985489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:02.734259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:04.386993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:05.964113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:07.644804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:09.315430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:10.979365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:12.640574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:14.299233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:16.018876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:17.676043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:19.427663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:21.089088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:22.816570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:24.680710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:26.330019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:28.024004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:51.375276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:52.951866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:54.583339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:56.132717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:57.701403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:59.301257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:01.051503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:02.803458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:04.450347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:06.030139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:07.711125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:09.378973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:11.044959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:12.704411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:14.363448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:16.083317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:17.743099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:19.497549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:21.149694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:22.889715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:24.752539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:26.388747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:28.097961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:51.447193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:53.021231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:54.651765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:56.204560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:57.768914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:59.371729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:01.125412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:02.878681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:04.520853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:06.100707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:07.781729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:09.453010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:11.115986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:12.776400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:14.434541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:16.157991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:17.814722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:19.571313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:21.216789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:22.964273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:24.827736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:26.456329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:28.171288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:51.520858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:53.087615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:54.721812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:56.363502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:57.833894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:59.441146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:01.199118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:02.956050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:04.590493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:06.268398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:07.857945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:09.531060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:11.188199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:12.847752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:14.506338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:16.229391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:17.988699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:19.646841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:21.286290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:23.039243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:24.902520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:26.525260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:28.328216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:51.586155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:53.147155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:54.786373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:56.426798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:57.895183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:19:59.504665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:01.265840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:03.024770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:04.654658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:06.333800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:07.922675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:09.597277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:11.253208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:12.913585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:14.572155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:16.295447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:18.054139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:19.713710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:21.347707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:23.105514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:24.975687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-25T15:20:26.588741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-25T15:20:37.542882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Acccident_CityAcccident_StateAccident_HourAccident_LocationAccident_SeverityAccident_TypeAge_InsuredAnnual_MileageAuto_MakeAuto_ModelAuto_YearBodily_InjuriesCapital_GainsCapital_LossCollision_TypeCommute_DiscountCustomer_Life_Value1DiffIN_MileageEducationGarage_LocationGenderHobbiesInjury_ClaimInsured_RelationshipInsured_ZipLow_Mileage_DiscountNum_of_Vehicles_InvolvedOccupationPolice_ReportPolicy_BI_HighPolicy_BI_LowPolicy_DedPolicy_NumPolicy_PremiumPolicy_StateProperty_ClaimProperty_DamageTotal_ClaimUmbrella_LimitVehicle_ClaimVehicle_ColorVehicle_CostVehicle_RegistrationWitnessesauthorities_contacted
Acccident_City1.0000.0110.022-0.0600.0630.0810.005-0.005-0.0010.019-0.0130.071-0.024-0.0380.0800.0140.000-0.0010.0720.0730.0770.1380.0050.076-0.0040.0000.0750.1050.0710.0620.0620.064-0.0440.0530.082-0.0020.1030.0030.0110.0020.032-0.012-0.0030.0950.089
Acccident_State0.0111.000-0.0440.0460.0830.0800.0050.0010.076-0.030-0.0130.0780.019-0.0570.0950.007-0.0070.0080.0850.0430.1020.152-0.0060.084-0.0230.0000.0830.1210.0830.0630.0630.083-0.0570.0430.0700.0000.061-0.002-0.0740.0040.035-0.017-0.0040.0800.078
Accident_Hour0.022-0.0441.0000.0030.2180.2820.1050.001-0.004-0.051-0.0690.106-0.013-0.0320.2910.012-0.0070.0010.0910.0640.0810.1310.0010.0970.0130.0120.1500.1030.0860.0790.0790.1280.034-0.0000.102-0.0030.086-0.002-0.015-0.0050.023-0.0750.0010.1100.199
Accident_Location-0.0600.0460.0031.0000.0910.088-0.011-0.0010.034-0.015-0.0040.0650.030-0.0280.0860.003-0.0010.0030.0950.1190.0810.1430.0060.088-0.0300.0000.0980.1190.0980.0840.0840.0870.0610.0070.0940.0030.0980.0090.0090.0060.035-0.013-0.0040.1060.080
Accident_Severity0.0630.0830.2180.0911.0000.4260.1060.0000.0750.0870.0920.0280.0870.0870.4260.0370.0000.0150.0740.0990.0400.1370.0070.0640.0660.0040.1770.1140.0610.0550.0550.0250.0930.0760.0360.0090.0840.0040.1040.0000.0280.0820.0000.0590.314
Accident_Type0.0810.0800.2820.0880.4261.0000.0890.0060.0810.1120.0780.0530.0950.0830.5790.0240.0000.0000.0790.0790.0500.1420.0000.0590.0710.0110.5770.1170.0590.0560.0560.0610.0960.0940.0430.0000.0350.0110.0650.0000.0130.0710.0000.0470.447
Age_Insured0.0050.0050.105-0.0110.1060.0891.0000.0050.0130.018-0.0140.082-0.021-0.0020.1140.0000.0010.0040.0920.1170.1260.1300.0010.098-0.0010.0000.1210.1110.0730.0890.0890.0870.0530.0290.0800.0130.0880.010-0.0040.0080.037-0.0020.0040.1040.091
Annual_Mileage-0.0050.0010.001-0.0010.0000.0060.0051.000-0.009-0.001-0.0010.0090.0100.0050.0070.009-0.006-0.0030.0040.0140.0050.0090.0050.000-0.0030.9780.0080.0000.0000.0090.0090.0000.005-0.0040.0000.0080.0000.0060.0140.0030.0040.003-0.0000.0080.000
Auto_Make-0.0010.076-0.0040.0340.0750.0810.013-0.0091.000-0.186-0.0020.0570.040-0.0450.0820.014-0.0030.0030.1030.1200.0580.1440.0040.099-0.0290.0100.0830.1030.0860.0790.0790.090-0.0260.0100.1170.0010.0820.0010.0020.0070.262-0.0820.0020.0830.094
Auto_Model0.019-0.030-0.051-0.0150.0870.1120.018-0.001-0.1861.0000.0100.0580.040-0.0330.0790.0070.0020.0030.0970.1540.0740.149-0.0080.0940.0340.0110.1120.1080.1010.0910.0910.097-0.031-0.0300.103-0.0110.072-0.0110.023-0.0030.2410.114-0.0040.0770.091
Auto_Year-0.013-0.013-0.069-0.0040.0920.078-0.014-0.001-0.0020.0101.0000.0990.0040.0870.0880.0170.0120.0050.0970.0930.1190.135-0.0030.0990.0240.0000.0770.1250.1190.0710.0710.0990.0090.0190.094-0.0100.106-0.0120.022-0.0030.0440.9300.0010.0720.100
Bodily_Injuries0.0710.0780.1060.0650.0280.0530.0820.0090.0570.0580.0991.0000.1090.0700.0240.0000.0000.0000.0730.0330.0320.1150.0000.0600.0670.0000.0490.1500.0350.0360.0360.0470.0910.0690.0650.0000.0400.0090.1070.0060.0130.0690.0000.0640.055
Capital_Gains-0.0240.019-0.0130.0300.0870.095-0.0210.0100.0400.0400.0040.1091.000-0.0470.0760.000-0.006-0.0110.1060.0600.0720.152-0.0020.0860.0150.0090.1110.1070.0810.0910.0910.090-0.019-0.0080.094-0.0040.091-0.006-0.046-0.0070.0360.017-0.0030.0980.080
Capital_Loss-0.038-0.057-0.032-0.0280.0870.083-0.0020.005-0.045-0.0330.0870.070-0.0471.0000.1010.000-0.0030.0010.1010.0920.1090.1410.0050.1050.0380.0060.0780.1110.1030.0750.0750.086-0.0110.0300.1020.0090.1180.010-0.0170.0120.0270.082-0.0030.0940.097
Collision_Type0.0800.0950.2910.0860.4260.5790.1140.0070.0820.0790.0880.0240.0760.1011.0000.0270.0050.0020.0940.0700.0390.1470.0000.0660.0420.0060.2350.1100.0630.0710.0710.0440.0840.0880.0560.0000.0360.0110.0710.0060.0300.0890.0000.0740.444
Commute_Discount0.0140.0070.0120.0030.0370.0240.0000.0090.0140.0070.0170.0000.0000.0000.0271.0000.0000.0000.0000.0080.0000.0330.0000.0090.0000.0020.0100.0040.0000.0000.0000.0020.0030.0030.0000.0230.0000.0090.0000.0000.0000.0000.0040.0080.016
Customer_Life_Value10.000-0.007-0.007-0.0010.0000.0000.001-0.006-0.0030.0020.0120.000-0.006-0.0030.0050.0001.0000.0060.0030.0130.0100.000-0.0000.0000.0040.0150.0000.0000.0080.0060.0060.0000.0030.0010.0000.0000.000-0.0020.0040.0020.0000.013-0.0080.0000.010
DiffIN_Mileage-0.0010.0080.0010.0030.0150.0000.004-0.0030.0030.0030.0050.000-0.0110.0010.0020.0000.0061.0000.0000.0120.0100.0090.0060.0000.0020.0000.0000.0080.0050.0000.0000.0080.002-0.0020.0070.0030.0000.0050.0040.0020.0090.004-0.0070.0000.000
Education0.0720.0850.0910.0950.0740.0790.0920.0040.1030.0970.0970.0730.1060.1010.0940.0000.0030.0001.0000.0720.0510.1340.0000.0870.0550.0000.0830.1250.0920.0980.0980.0790.1120.0880.0860.0070.0770.0080.0770.0000.0280.0850.0000.0950.061
Garage_Location0.0730.0430.0640.1190.0990.0790.1170.0140.1200.1540.0930.0330.0600.0920.0700.0080.0130.0120.0721.0000.0100.1250.0060.0430.0520.0000.0660.0860.0330.0330.0330.0590.0980.0890.0250.0000.0280.0050.1830.0000.0350.0670.0000.0420.059
Gender0.0770.1020.0810.0810.0400.0500.1260.0050.0580.0740.1190.0320.0720.1090.0390.0000.0100.0100.0510.0101.0000.1220.0000.0330.0720.0080.0510.0610.0280.0800.0800.0170.1100.1540.0270.0090.0200.0000.0530.0110.0220.0790.0100.0630.089
Hobbies0.1380.1520.1310.1430.1370.1420.1300.0090.1440.1490.1350.1150.1520.1410.1470.0330.0000.0090.1340.1250.1221.0000.0000.1410.1380.0000.1300.1450.1690.1330.1330.1090.1210.1260.1490.0000.1140.0060.1380.0070.0500.1140.0000.1400.126
Injury_Claim0.005-0.0060.0010.0060.0070.0000.0010.0050.004-0.008-0.0030.000-0.0020.0050.0000.000-0.0000.0060.0000.0060.0000.0001.0000.0000.0010.0000.0000.0080.0000.0080.0080.0000.0010.0040.0000.1440.0060.6010.0110.1480.002-0.007-0.0060.0000.000
Insured_Relationship0.0760.0840.0970.0880.0640.0590.0980.0000.0990.0940.0990.0600.0860.1050.0660.0090.0000.0000.0870.0430.0330.1410.0001.0000.0590.0000.0670.1260.0610.0830.0830.0550.0980.0720.0460.0000.0560.0090.0990.0050.0290.1080.0080.0640.078
Insured_Zip-0.004-0.0230.013-0.0300.0660.071-0.001-0.003-0.0290.0340.0240.0670.0150.0380.0420.0000.0040.0020.0550.0520.0720.1380.0010.0591.0000.0000.0700.1380.0890.0340.0340.0550.0290.0400.0720.0030.072-0.0020.001-0.0080.0340.0230.0110.0510.056
Low_Mileage_Discount0.0000.0000.0120.0000.0040.0110.0000.9780.0100.0110.0000.0000.0090.0060.0060.0020.0150.0000.0000.0000.0080.0000.0000.0000.0001.0000.0060.0040.0000.0000.0000.0000.0150.0000.0000.0000.0000.0110.0090.0110.0000.0000.0000.0070.000
Num_of_Vehicles_Involved0.0750.0830.1500.0980.1770.5770.1210.0080.0830.1120.0770.0490.1110.0780.2350.0100.0000.0000.0830.0660.0510.1300.0000.0670.0700.0061.0000.1120.0510.0400.0400.0740.0920.0670.0440.0000.0400.0060.0960.0000.0200.0890.0080.0440.178
Occupation0.1050.1210.1030.1190.1140.1170.1110.0000.1030.1080.1250.1500.1070.1110.1100.0040.0000.0080.1250.0860.0610.1450.0080.1260.1380.0040.1121.0000.1170.1250.1250.1440.1280.1200.0890.0070.0790.0040.1160.0000.0340.0980.0070.1160.105
Police_Report0.0710.0830.0860.0980.0610.0590.0730.0000.0860.1010.1190.0350.0810.1030.0630.0000.0080.0050.0920.0330.0280.1690.0000.0610.0890.0000.0510.1171.0000.0560.0560.0300.1160.1070.0580.0110.0550.0090.1150.0100.0240.0810.0000.0450.056
Policy_BI_High0.0620.0630.0790.0840.0550.0560.0890.0090.0790.0910.0710.0360.0910.0750.0710.0000.0060.0000.0980.0330.0800.1330.0080.0830.0340.0000.0400.1250.0561.0001.0000.0300.1010.1270.0320.0060.0180.0000.0740.0000.0190.0940.0000.0590.078
Policy_BI_Low0.0620.0630.0790.0840.0550.0560.0890.0090.0790.0910.0710.0360.0910.0750.0710.0000.0060.0000.0980.0330.0800.1330.0080.0830.0340.0000.0400.1250.0561.0001.0000.0300.1010.1270.0320.0060.0180.0000.0740.0000.0190.0940.0000.0590.078
Policy_Ded0.0640.0830.1280.0870.0250.0610.0870.0000.0900.0970.0990.0470.0900.0860.0440.0020.0000.0080.0790.0590.0170.1090.0000.0550.0550.0000.0740.1440.0300.0300.0301.0000.1000.1080.0420.0000.0220.0070.0950.0000.0240.0980.0000.0680.066
Policy_Num-0.044-0.0570.0340.0610.0930.0960.0530.005-0.026-0.0310.0090.091-0.019-0.0110.0840.0030.0030.0020.1120.0980.1100.1210.0010.0980.0290.0150.0920.1280.1160.1010.1010.1001.0000.0100.085-0.0020.076-0.001-0.006-0.0020.0290.024-0.0010.1040.104
Policy_Premium0.0530.043-0.0000.0070.0760.0940.029-0.0040.010-0.0300.0190.069-0.0080.0300.0880.0030.001-0.0020.0880.0890.1540.1260.0040.0720.0400.0000.0670.1200.1070.1270.1270.1080.0101.0000.0880.0050.1110.006-0.0030.0030.035-0.0060.0100.1020.097
Policy_State0.0820.0700.1020.0940.0360.0430.0800.0000.1170.1030.0940.0650.0940.1020.0560.0000.0000.0070.0860.0250.0270.1490.0000.0460.0720.0000.0440.0890.0580.0320.0320.0420.0850.0881.0000.0120.0590.0000.0950.0070.0280.0940.0000.0340.046
Property_Claim-0.0020.000-0.0030.0030.0090.0000.0130.0080.001-0.011-0.0100.000-0.0040.0090.0000.0230.0000.0030.0070.0000.0090.0000.1440.0000.0030.0000.0000.0070.0110.0060.0060.000-0.0020.0050.0121.0000.0030.602-0.0030.1470.004-0.014-0.0120.0060.006
Property_Damage0.1030.0610.0860.0980.0840.0350.0880.0000.0820.0720.1060.0400.0910.1180.0360.0000.0000.0000.0770.0280.0200.1140.0060.0560.0720.0000.0400.0790.0550.0180.0180.0220.0760.1110.0590.0031.0000.0080.0970.0000.0170.0750.0000.0400.031
Total_Claim0.003-0.002-0.0020.0090.0040.0110.0100.0060.001-0.011-0.0120.009-0.0060.0100.0110.009-0.0020.0050.0080.0050.0000.0060.6010.009-0.0020.0110.0060.0040.0090.0000.0000.007-0.0010.0060.0000.6020.0081.0000.0030.6020.001-0.016-0.0110.0040.005
Umbrella_Limit0.011-0.074-0.0150.0090.1040.065-0.0040.0140.0020.0230.0220.107-0.046-0.0170.0710.0000.0040.0040.0770.1830.0530.1380.0110.0990.0010.0090.0960.1160.1150.0740.0740.095-0.006-0.0030.095-0.0030.0970.0031.000-0.0020.0320.022-0.0010.0770.079
Vehicle_Claim0.0020.004-0.0050.0060.0000.0000.0080.0030.007-0.003-0.0030.006-0.0070.0120.0060.0000.0020.0020.0000.0000.0110.0070.1480.005-0.0080.0110.0000.0000.0100.0000.0000.000-0.0020.0030.0070.1470.0000.602-0.0021.0000.000-0.004-0.0060.0000.008
Vehicle_Color0.0320.0350.0230.0350.0280.0130.0370.0040.2620.2410.0440.0130.0360.0270.0300.0000.0000.0090.0280.0350.0220.0500.0020.0290.0340.0000.0200.0340.0240.0190.0190.0240.0290.0350.0280.0040.0170.0010.0320.0001.0000.0360.0080.0160.030
Vehicle_Cost-0.012-0.017-0.075-0.0130.0820.071-0.0020.003-0.0820.1140.9300.0690.0170.0820.0890.0000.0130.0040.0850.0670.0790.114-0.0070.1080.0230.0000.0890.0980.0810.0940.0940.0980.024-0.0060.094-0.0140.075-0.0160.022-0.0040.0361.000-0.0010.0920.071
Vehicle_Registration-0.003-0.0040.001-0.0040.0000.0000.004-0.0000.002-0.0040.0010.000-0.003-0.0030.0000.004-0.008-0.0070.0000.0000.0100.000-0.0060.0080.0110.0000.0080.0070.0000.0000.0000.000-0.0010.0100.000-0.0120.000-0.011-0.001-0.0060.008-0.0011.0000.0050.000
Witnesses0.0950.0800.1100.1060.0590.0470.1040.0080.0830.0770.0720.0640.0980.0940.0740.0080.0000.0000.0950.0420.0630.1400.0000.0640.0510.0070.0440.1160.0450.0590.0590.0680.1040.1020.0340.0060.0400.0040.0770.0000.0160.0920.0051.0000.061
authorities_contacted0.0890.0780.1990.0800.3140.4470.0910.0000.0940.0910.1000.0550.0800.0970.4440.0160.0100.0000.0610.0590.0890.1260.0000.0780.0560.0000.1780.1050.0560.0780.0780.0660.1040.0970.0460.0060.0310.0050.0790.0080.0300.0710.0000.0611.000

Missing values

2025-07-25T15:20:28.522462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-25T15:20:28.886443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Claim_IDBind_Date1Customer_Life_Value1Age_InsuredPolicy_NumPolicy_StatePolicy_Start_DatePolicy_Expiry_DatePolicy_DedPolicy_PremiumUmbrella_LimitInsured_ZipGenderEducationOccupationHobbiesInsured_RelationshipCapital_GainsCapital_LossGarage_LocationAccident_DateAccident_TypeCollision_TypeAccident_Severityauthorities_contactedAcccident_StateAcccident_CityAccident_LocationAccident_HourNum_of_Vehicles_InvolvedProperty_DamageBodily_InjuriesWitnessesPolice_ReportDL_Expiry_DateClaims_DateAuto_MakeAuto_ModelAuto_YearVehicle_ColorVehicle_CostAnnual_MileageDiffIN_MileageLow_Mileage_DiscountCommute_DiscountTotal_ClaimInjury_ClaimProperty_ClaimVehicle_ClaimVehicle_RegistrationCheck_PointPolicy_BI_LowPolicy_BI_High
0AA000000012023-01-01-1.601757-1.198198-1.2009011.1831012023-10-132024-04-13-0.221188-1.096695-0.479633-0.400156femalehigh schooladm-clericalpoloother-relative1.3540150.952504no2024-02-16single vehicle collisionrear collisiontotal lossambulance0.813228-0.9869380.958104-0.091377-0.825611no-1.2262550.464048yes2025-08-122024-02-17-0.139003-1.021558-1.232989white-1.0443650.953796-1.437938-0.485266-0.1413-0.664749-0.6868630.121189-0.769407-0.6425480.01.4082111.462041
1AA000000022023-01-01-1.601757-0.869785-0.998829-1.2329932023-10-212024-04-21-1.039419-0.375026-0.4796331.442688maleassociateprotective-servmovieshusband0.4534110.952504no2024-02-21multi-vehicle collisionside collisionmajor damageother-1.506304-1.4785940.8541231.3471772.120281yes1.222032-1.329981not available2026-04-152024-02-26-1.384325-1.2918330.521456black0.081907-1.516977-0.4324032.060726-0.14130.5505860.5349570.1793960.397323-1.1669260.0-0.139861-0.278805
2AA000000032022-07-010.0001941.210163-0.3134821.1831012023-11-262024-05-26-1.039419-1.493144-0.479633-0.585494femalemasterspriv-house-servboard-gamesother-relative-0.9046990.952504no2024-02-26multi-vehicle collisionrear collisiontotal losspolice-0.114585-0.003626-0.3277970.3401891.138317no-1.2262550.464048no2026-04-242024-03-01-1.384325-1.3819241.574123gray1.505569-0.263331-1.526203-0.485266-0.1413-0.265616-0.171883-0.206824-0.156957-0.0353770.01.4082111.462041
3AA000000042023-01-01-1.601757-0.2129590.311058-1.2329932023-08-082024-02-08-0.221188-0.794636-0.479633-0.415983malephdtech-supportreadingown-child-0.904699-0.880987no2024-01-09single vehicle collisionrear collisionmajor damageambulance0.349322-0.986938-1.6587560.484045-0.825611yes1.2220321.361062not available2026-03-172024-01-131.106319-0.0305521.574123gray1.5386850.023491-1.480628-0.485266-0.14130.2963780.168872-0.4837290.8926900.5700610.01.4082111.462041
4AA000000052022-03-011.068161-1.1981981.1757701.1831012023-11-122024-05-12-0.221188-0.084772-0.479633-0.806685malemdexec-managerialcampinghusband-0.904699-0.190313no2024-02-17multi-vehicle collisionside collisiontotal lossother-0.578491-0.495282-1.4715901.2033211.138317unknown-1.226255-0.432966not available2025-11-012024-02-201.6044480.7802710.521456gray0.204020-1.474860-0.6354722.060726-0.14130.9026450.456859-0.2749211.6069831.0428240.0-0.139861-0.278805
5AA000000062022-08-01-0.266798-0.7603141.309629-1.2329932023-11-062024-05-06-1.0394191.369432-0.479633-0.377788malemastersprof-specialtyyachtinghusband1.217123-0.083508no2024-02-06single vehicle collisionrear collisiontotal lossambulance1.277135-0.9869380.0950601.491032-0.825611unknown1.2220321.361062yes2025-12-162024-02-100.1100621.5010031.574123black1.356879-0.7927251.011574-0.485266-0.1413-0.962337-0.792787-0.375056-0.7705291.3814400.01.4082111.462041
6AA000000072022-07-010.0001940.115454-0.371414-0.0249462023-10-162024-04-16-1.0394190.846559-0.479633-0.987965femalejdsalesskydivingown-child-0.9046990.952504no2024-02-11multi-vehicle collisionrear collisiontotal losspolice-1.5063041.471342-0.3624571.2033211.138317unknown-1.226255-1.329981yes2026-03-092024-02-11-0.6371320.690180-0.882100black-0.8609211.5863491.064072-0.485266-0.14130.4446010.046744-0.2458981.069497-0.0161510.0-1.068703-0.975144
7AA000000082022-02-011.335152-0.7603141.475834-0.0249462023-10-142024-04-14-0.221188-1.086926-0.4796331.572554femalehigh schoolcraft-repairgolfunmarried0.0895670.952504no2024-01-23single vehicle collisionfront collisiontotal lossother-1.042398-0.986938-1.014072-0.091377-0.825611unknown1.2220321.361062yes2025-10-302024-01-26-0.6371320.6901800.521456silver0.110324-0.5570831.415980-0.485266-0.1413-0.708968-0.729844-0.7504450.033305-0.6258330.0-0.139861-0.278805
8AA000000092022-11-01-1.067774-0.760314-1.341107-1.2329932023-08-262024-02-26-1.0394191.450920-0.479633-0.622685femalemdother-serviceyachtingown-child0.4065800.952504no2024-01-08single vehicle collisionside collisiontotal lossfire0.8132281.471342-1.298288-1.386075-0.825611unknown1.222032-0.432966yes2025-06-112024-01-11-1.135261-1.7422901.574123black1.423741-0.9268060.929655-0.485266-0.14130.342416-0.1199150.4385130.3613860.9396810.01.4082111.462041
9AA000000102022-05-010.5341770.115454-0.820671-1.2329932023-10-052024-04-05-0.2211880.429351-0.4796331.442116malephdhandlers-cleanerscampingunmarried0.5110500.952504no2024-02-15single vehicle collisionrear collisionminor damageambulance-0.578491-0.0036260.8263940.052478-0.825611yes-1.2262550.464048yes2025-11-122024-02-191.106319-0.3909180.521456gray0.224114-1.378364-0.3683672.060726-0.1413-0.798974-0.562294-0.742656-0.3115100.4590370.01.4082111.462041
Claim_IDBind_Date1Customer_Life_Value1Age_InsuredPolicy_NumPolicy_StatePolicy_Start_DatePolicy_Expiry_DatePolicy_DedPolicy_PremiumUmbrella_LimitInsured_ZipGenderEducationOccupationHobbiesInsured_RelationshipCapital_GainsCapital_LossGarage_LocationAccident_DateAccident_TypeCollision_TypeAccident_Severityauthorities_contactedAcccident_StateAcccident_CityAccident_LocationAccident_HourNum_of_Vehicles_InvolvedProperty_DamageBodily_InjuriesWitnessesPolice_ReportDL_Expiry_DateClaims_DateAuto_MakeAuto_ModelAuto_YearVehicle_ColorVehicle_CostAnnual_MileageDiffIN_MileageLow_Mileage_DiscountCommute_DiscountTotal_ClaimInjury_ClaimProperty_ClaimVehicle_ClaimVehicle_RegistrationCheck_PointPolicy_BI_LowPolicy_BI_High
39990AA000399912022-03-011.068161-0.1034880.058593-1.2329932023-09-192024-03-191.4152731.156880-0.479633-0.523956femalecollegeadm-clericalskydivingother-relative-0.904699-1.699828no2024-02-01single vehicle collisionfront collisionmajor damageother0.813228-0.9869380.0118751.203321-0.825611yes1.222032-1.329981no2025-05-112024-02-01-0.8861961.140637-0.180322red-0.4994951.473593-1.264868-0.485266-0.14130.283506-0.511730-0.5538401.586139-1.4635400.0-1.068703-0.975144
39991AA000399922022-01-011.6021440.443866-1.4090321.1831012023-09-272024-03-27-0.2211880.535423-0.4796331.505662femaleassociatesalesbase-jumpingnot-in-family-0.9046990.952504no2024-01-11multi-vehicle collisionside collisionminor damagefire-1.0423980.979686-1.7038141.0594661.138317yes-0.0021121.361062not available2025-08-162024-01-140.1100621.501003-0.180322black-0.4568500.8077190.632552-0.485266-0.1413-0.856411-0.520782-0.774193-0.433490-0.6352730.01.4082111.462041
39992AA000399932022-09-01-0.5337900.115454-0.0412911.1831012023-09-182024-03-18-1.039419-0.070485-0.479633-0.456185femalehigh schooltransport-movingbungie-jumpinghusband-0.904699-0.635335no2024-01-27vehicle theftunknowntrivial damagepolice1.2771351.4713421.658243-0.954509-0.825611yes-0.002112-0.432966no2026-02-172024-01-280.857255-1.4720160.872345gray0.616196-0.923074-1.133335-0.485266-0.1413-1.014466-0.785527-0.754291-0.5108040.2879970.0-1.068703-0.975144
39993AA000399942023-01-01-1.601757-2.073965-0.173177-0.0249462023-10-122024-04-12-0.221188-0.2685881.2727711.560562femalehigh schoolcraft-repairgolfother-relative-0.904699-0.994913no2024-02-01single vehicle collisionside collisionminor damageother1.277135-0.986938-1.3502781.491032-0.825611no-0.0021121.361062yes2026-02-262024-02-01-1.384325-1.2918330.170567gray-0.1353381.424812-0.094341-0.485266-0.14130.2295220.1191000.0787480.2610870.9348310.01.4082111.462041
39994AA000399952022-04-010.801169-0.103488-1.681412-0.0249462023-10-212024-04-21-1.0394190.609503-0.479633-0.502565femalephdcraft-repaircampingunmarried-0.9046990.952504no2024-02-01single vehicle collisionrear collisiontotal losspolice0.349322-1.4785941.710234-0.235232-0.825611unknown-1.226255-0.432966no2025-05-292024-02-011.6044480.780271-0.882100gray-0.907966-0.903082-0.171645-0.485266-0.14130.1106200.829841-0.293505-0.289747-1.2013940.0-0.139861-0.278805
39995AA000399962022-04-010.8011690.2249240.689592-1.2329932023-09-222024-03-22-0.221188-1.785597-0.479633-0.573864malehigh schoolsalescross-fitunmarried1.3360030.952504no2024-01-03multi-vehicle collisionrear collisiontotal lossambulance0.349322-0.986938-0.5218950.7717551.138317unknown-0.002112-0.432966no2025-11-202024-01-040.3591260.4199051.223234black0.8894300.3172431.563090-0.485266-0.14130.5365451.1454770.706953-0.727466-0.0272360.0-0.139861-0.278805
39996AA000399972022-03-011.068161-0.4319010.688867-1.2329932023-08-112024-02-11-0.2211880.367563-0.479633-0.458891malemastersfarming-fishingcross-fitnot-in-family0.712785-2.301497no2024-01-03single vehicle collisionfront collisiontotal lossambulance1.277135-1.4785940.393139-0.091377-0.825611no-1.2262550.464048not available2025-06-182024-01-07-1.1352611.5910940.872345gray3.4514100.739479-0.658548-0.485266-0.1413-0.643235-0.140049-0.582225-0.564864-0.4802540.01.4082111.462041
39997AA000399982022-09-01-0.533790-0.322430-0.169544-1.2329932023-09-202024-03-201.4152730.2534723.4632771.399097femalecollegeprof-specialtykayakingnot-in-family-0.904699-1.845795no2024-01-30parked carunknowntrivial damageunknown1.277135-1.4785940.022273-0.379088-0.825611no-1.226255-0.432966yes2025-08-132024-01-30-0.6371320.690180-0.531211white-0.6764520.029355-1.172564-0.485266-0.1413-1.056735-0.800043-0.719342-0.614123-1.4201520.01.4082111.462041
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